Evaluating a novel 24-hour rest/activity rhythm marker of preclinical β-amyloid deposition

STUDY OBJECTIVES

To compare sleep and 24-hour rest/activity rhythms (RARs) between cognitively normal older adults who are β-amyloid-positive (Aβ+) or Aβ- and replicate a novel time-of-day-specific difference between these groups identified in a previous exploratory study.

METHODS

We studied 82 cognitively normal participants from the Baltimore Longitudinal Study of Aging (aged 75.7 ± 8.5 years, 55% female, 76% white) with wrist actigraphy data and Aβ+ versus Aβ- status measured by [11C] Pittsburgh compound B positron emission tomography. RARs were calculated using epoch-level activity count data from actigraphy. We used novel, data-driven function-on-scalar regression analyses and standard RAR metrics to cross-sectionally compare RARs between 25 Aβ+ and 57 Aβ- participants.

RESULTS

Compared to Aβ- participants, Aβ+ participants had higher mean activity from 1:00 p.m. to 3:30 p.m. when using less conservative pointwise confidence intervals (CIs) and from 1:30 p.m. to 2:30 p.m. using more conservative, simultaneous CIs. Furthermore, Aβ+ participants had higher day-to-day variability in activity from 9:00 a.m. to 11:30 a.m. and lower variability from 1:30 p.m. to 4:00 p.m. and 7:30 p.m. to 10:30 p.m. according to pointwise CIs, and lower variability from 8:30 p.m. to 10:00 p.m. using simultaneous CIs. There were no Aβ-related differences in standard sleep or RAR metrics.

CONCLUSIONS

Findings suggest Aβ+ older adults have higher, more stable day-to-day afternoon/evening activity than Aβ- older adults, potentially reflecting circadian dysfunction. Studies are needed to replicate our findings and determine whether these or other time-of-day-specific RAR features have utility as markers of preclinical Aβ deposition and if they predict clinical dementia and agitation in the afternoon/evening (i.e. "sundowning").

Filtering walking actigraphy data in children with unilateral cerebral palsy: A preliminary study

This study aimed to determine whether filtering out walking-related actigraphy data improves the reliability and accuracy of real-world upper extremity activity assessment in children with unilateral cerebral palsy. Twenty-two children aged 4-12 years diagnosed with unilateral cerebral palsy were included in this study, which was drawn from a two-phase randomized controlled trial conducted from July 2021 to December 2022. Data were collected from a tertiary hospital in Seoul, Republic of Korea. Participants were monitored using tri-axial accelerometers on both wrists across three time points (namely, T0, T1, and T2) over 3 days; interventions were used between each time point. Concurrently, an in-laboratory study focusing on walking and bimanual tasks was conducted with four participants. Data filtration resulted in a reduction of 8.20% in total data entry. With respect to reliability assessment, the intra-class correlation coefficients indicated enhanced consistency after filtration, with increased values for both the affected and less-affected sides. Before filtration, the magnitude counts for both sides showed varying tendencies, depending on the time points; however, they presented a consistent and stable trend after filtration. The findings of this research underscore the importance of accurately interpreting actigraphy measurements in children with unilateral cerebral palsy for targeted upper limb intervention by filtering walking-induced data.

Wrist-worn actigraphy in agitated late-stage dementia patients: A feasibility study on digital inclusion

BACKGROUND

Wrist-worn actigraphy can be an objective tool to assess sleep and other behavioral and psychological symptoms in dementia (BPSD). We investigated the feasibility of using wearable actigraphy in agitated late-stage dementia patients.

METHODS

Agitated, late-stage Alzheimer's dementia care home residents in Greater London area (n = 29; 14 females, mean age ± SD: 80.8 ± 8.2; 93.1% White) were recruited to wear an actigraphy watch for 4 weeks. Wearing time was extracted to evaluate compliance, and factors influencing compliance were explored.

RESULTS

A high watch-acceptance (96.6%) and compliance rate (88.0%) was noted. Non-compliance was not associated with age or BPSD symptomatology. However, participants with "better" cognitive function (R = 0.42, p = 0.022) and during nightshift (F1.240, 33.475 = 8.075, p = 0.005) were less compliant. Female participants were also marginally less compliant (F1, 26 = 3.790, p = 0.062).

DISCUSSIONS

Wrist-worn actigraphy appears acceptable and feasible in late-stage agitated dementia patients. Accommodating the needs of both the patients and their carers may further improve compliance.

Statistical analysis of actigraphy data with generalised additive models

There is a growing interest in the use of physical activity data in clinical studies, particularly in diseases that limit mobility in patients. High-frequency data collected with digital sensors are typically summarised into actigraphy features aggregated at epoch level (e.g., by minute). The statistical analysis of such volume of data is not straightforward. The general trend is to derive metrics, capturing specific aspects of physical activity, that condense (say) a week worth of data into a single numerical value. Here we propose to analyse the entire time-series data using Generalised Additive Models (GAMs). GAMs are semi-parametric models that allow inclusion of both parametric and non-parametric terms in the linear predictor. The latter are smooth terms (e.g., splines) and, in the context of actigraphy minute-by-minute data analysis, they can be used to assess daily patterns of physical activity. This in turn can be used to better understand changes over time in longitudinal studies as well as to compare treatment groups. We illustrate the application of GAMs in two clinical studies where actigraphy data was collected: a non-drug, single-arm study in patients with amyotrophic lateral sclerosis, and a physical-activity sub-study included in a phase 2b clinical trial in patients with chronic obstructive pulmonary disease.

External validity of the reduced Morningness-Eveningness Questionnaire for Children and Adolescents: an actigraphic study

The aim of this study was to examine the external validity of the reduced Morningness-Eveningness Questionnaires for Children and Adolescents, using circadian motor activity, assessed through actigraphy, as an external criterion. Overall, 458 participants (269 females), with a mean (standard deviation) age of 15.75 (1.16) years, took part in this study. Each adolescent was requested to wear the actigraph Micro Motionlogger Watch actigraph (Ambulatory Monitoring, Inc., Ardlsey, NY, USA) around the non-dominant wrist for 1 week. At the end of the actigraphic recording, participants completed the reduced Morningness-Eveningness Questionnaires for Children and Adolescents. We extracted the motor activity counts, minute-by-minute over the 24 h, to depict the 24-h motor activity pattern, and we adopted the statistical framework of functional linear modelling to examine its changes according to chronotype. According to the reduced Morningness-Eveningness Questionnaires for Children and Adolescents cut-off scores, 13.97% of participants (n = 64) belonged to the evening-types category, 9.39% (n = 43) to morning-types, while the remaining (76.64%, n = 351) to the intermediate-types category. Evening types moved significantly more than the intermediate and morning types from around 10:00 p.m. to 2:00 a.m., while the opposite pattern of results was observed around 4:00 a.m. The results highlighted a significant difference in the 24-h motor activity pattern between chronotypes, in line with the expectations based on their well-known behaviour. Therefore, this study shows that the external validity of the reduced Morningness-Eveningness Questionnaire for Children and Adolescents, established by considering motor activity (recorded through actigraphy) as an external criterion, is satisfactory.

Wearable Sensors to Monitor Physical Activity in Heart Failure Clinical Trials: State-of-the-Art Review

BACKGROUND

Estimation of the effects that drugs or other interventions have on patients' symptoms and functions is crucial in heart failure trials. Traditional symptoms and functions clinical outcome assessments have important limitations. Actigraphy may help to overcome these limitations due to its objective nature and the potential for continuous recording of data. However, actigraphy is not currently accepted as clinically relevant by key stakeholders.

METHODS AND RESULTS

In this state-of-the-art study, the key aspects to consider when implementing actigraphy in heart failure trials are discussed. They include which actigraphy-derived measures should be considered, how to build endpoints using them, how to measure and analyze them, and how to handle the patients' and sites' logistics of integrating devices into trials. A comprehensive recommendation based on the current evidence is provided.

CONCLUSION

Actigraphy is technically feasible in clinical trials involving heart failure, but successful implementation and use to demonstrate clinically important differences in physical functioning with drug or other interventions require careful consideration of many design choices.

Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors

BACKGROUND

Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The former measures body movement and the latter measures body posture. The goal of the current study is to quantify the pattern and variation of movement (by ActiGraph activity counts) during activPAL-identified sitting events, and examine associations between patterns and health-related outcomes, such as systolic and diastolic blood pressure (SBP and DBP).

METHODS

The current study included 314 overweight postmenopausal women, who were instructed to wear an activPAL (at thigh) and ActiGraph (at waist) simultaneously for 24 hours a day for a week under free-living conditions. ActiGraph and activPAL data were processed to obtain minute-level time-series outputs. Multilevel functional principal component analysis (MFPCA) was applied to minute-level ActiGraph activity counts within activPAL-identified sitting bouts to investigate variation in movement while sitting across subjects and days. The multilevel approach accounted for the nesting of days within subjects.

RESULTS

At least 90% of the overall variation of activity counts was explained by two subject-level principal components (PC) and six day-level PCs, hence dramatically reducing the dimensions from the original minute-level scale. The first subject-level PC captured patterns of fluctuation in movement during sitting, whereas the second subject-level PC delineated variation in movement during different lengths of sitting bouts: shorter (< 30 minutes), medium (30 -39 minutes) or longer (> 39 minute). The first subject-level PC scores showed positive association with DBP (standardized β ^ : 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP.

CONCLUSION

In this work we implemented MFPCA to identify variation in movement patterns during sitting bouts, and showed that these patterns were associated with cardiovascular health. Unlike existing methods, MFPCA does not require pre-specified cut-points to define activity intensity, and thus offers a novel powerful statistical tool to elucidate variation in SB patterns and health.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684.

Identifying the most representative actigraphy variables reflecting standardized hand function assessments for remote monitoring in children with unilateral cerebral palsy

BACKGROUND

Accurate assessment of physical activity and motor function in children with cerebral palsy is crucial for determining the effectiveness of interventions. This study aimed to investigate the correlation between real-world activity monitoring outcomes and in-laboratory standardized hand function assessments in children with unilateral cerebral palsy.

METHODS

Actigraphy data were collected over 3 days from children aged 4-12 years with unilateral cerebral palsy before in-laboratory assessments. To tackle the high dimensionality and collinearity of actigraphy variables, we first applied hierarchical clustering using the Pearson correlation coefficient as the distance metric and then performed a principal component analysis (PCA) to reduce the dimensionality of our data.

RESULTS

Both hierarchical clustering and PCAs revealed a consistent pattern in which magnitude ratio variables (ln[affected side magnitude/less-affected side magnitude]) were more strongly associated with standardized assessments of hand function than with activity time and distance domain variables. Hierarchical clustering analysis identified two distinct clusters of actigraphy variables, with the second cluster primarily consisting of magnitude ratio variables that exhibited the strongest correlation with Melbourne Assessment 2, Pediatric Motor Activity Log, Assisting Hand Assessment, and Manual Ability Classification System level. Principal component 2, primarily representing the magnitude ratio domain, was positively associated with a meaningful portion of subcategories of standardized measures, whereas principal component 1, representing the activity time and distance component, showed limited associations.

CONCLUSIONS

The magnitude ratio of actigraphy can provide additional objective information that complements in-laboratory hand function assessment outcomes in future studies of children with unilateral cerebral palsy. TRIAL REGISTRATION IN CLINICALTRIALS.GOV: NCT04904796 (registered prospectively; date of registration: 23/05/2021).

Assessing the Feasibility and Efficacy of Pre-Sleep Dim Light Therapy for Adults with Insomnia: A Pilot Study

Background: Insomnia is increasingly recognized for its marked impact on public health and is often associated with various adverse health outcomes, including cardiovascular diseases and mental health disorders. The aim of this study was to investigate the efficacy of pre-sleep dim light therapy (LT) as a non-pharmacological intervention for insomnia in adults, assessing its influence on sleep parameters and circadian rhythms. Methods: A randomized, open-label, two-arm clinical trial was conducted over two weeks with 40 participants aged 20-60 years, all of whom had sleep disorders (CRIS, KCT0008501). They were allocated into control and LT groups. The LT group received exposure to warm-colored light, minimizing the blue spectrum, before bedtime. The study combined subjective evaluation via validated, sleep-related questionnaires, objective sleep assessments via actigraphy, and molecular analyses of circadian clock gene expression in peripheral blood mononuclear cells. Baseline characteristics between the two groups were compared using an independent t-test for continuous variables and the chi-squared test for categorical variables. Within-group differences were assessed using the paired t-test. Changes between groups were analyzed using linear regression, adjusting for each baseline value and body mass index. The patterns of changes in sleep parameters were calculated using a linear mixed model. Results: The LT group exhibited significant improvements in sleep quality (difference in difference [95% CI]; -2.00 [-3.58, -0.43], and sleep efficiency (LT: 84.98 vs. control: 82.11, p = 0.032), and an advanced Dim Light Melatonin Onset compared to the control group (approximately 30 min). Molecular analysis indicated a significant reduction in CRY1 gene expression after LT, suggesting an influence on circadian signals for sleep regulation. Conclusions: This study provides evidence for the efficacy of LT in improving sleep quality and circadian rhythm alignment in adults with insomnia. Despite limitations, such as a small sample size and short study duration, the results underscore the potential of LT as a viable non-pharmacological approach for insomnia. Future research should expand on these results with larger and more diverse cohorts followed over a longer period to validate and further elucidate the value of LT in sleep medicine. Trial registration: The trial was registered with the Clinical Research Information Service (KCT0008501).

Effectiveness of an intervention program on physical activity in children with narcolepsy type 1

OBJECTIVES

Physical activity (PA) is recommended as part of the management of narcolepsy type 1 (NT1). This study aimed at 1) characterizing PA in children and adolescents treated for NT1 using objective and subjective measurements, 2) evaluating how PA is associated with NT1 symptoms and comorbidities, and 3) evaluating the effects of an Adapted Physical Activity (APA) program on PA and clinical characteristics.

PATIENTS/METHODS

Patients with NT1 from the National Reference Center of Narcolepsy (Lyon, France) were consecutively included in an APA intervention protocol. Narcolepsy symptoms and comorbidities were collected using standardized questionnaires and sustained attention was evaluated using the Bron-Lyon Attention Stability Test before and after the four-week APA intervention. PA was measured objectively using actigraphy throughout the study.

RESULTS

Twenty-seven NT1 patients were included (median age 14.7 years [8.3-18.4], cataplexy 88.9%, obesity 37.0%). At baseline, 52.4% of the patients had satisfactory PA levels according to international recommendations. Patients with leisure-time PA (LTPA) showed higher quality of life than patients without. 45% of the patients increased PA during the intervention compared to baseline. These responsive patients had more depressive feelings and tended to have lower objective PA than non-responsive patients at baseline. No significant correlation was found between PA levels before and during the intervention and other clinical data.

CONCLUSIONS

Most children with NT1 showed satisfying PA levels despite their daytime sleepiness. LTPA engagement was associated with higher quality of life. An APA intervention could be effective in children with narcolepsy, especially for those with depressive feelings.

Comparing actigraphy and diary to measure daily and average sleep in firefighters: a Bland-Altman analysis

STUDY OBJECTIVES

This study sought to examine the relationship between actigraphy and the Consensus Sleep Diary to contribute information on their concurrent validity in a sample of career firefighters.

METHODS

Sixty firefighters were recruited from a large, urban fire department in the southwest United States that utilizes a fire-based emergency medical services system and a 5/6 shift schedule. A total of 329 differences were recorded during participants' 6-day between-shift recovery period. Data was collected utilizing the two most common forms of sleep analysis in an outpatient setting, wrist actigraphy (Actiwatch-2) and the Consensus Sleep Diary. Nine major sleep indices were computed: wake time after sleep onset, total sleep time, sleep onset latency, sleep offset, in-bed time, lights-off time, out-of-bed time, wake time, and sleep efficiency.

RESULTS

Firefighters overestimated sleep efficiency and underestimated wake after sleep onset by values that were greater than the American Academy of Sleep Medicine a priori clinical significance thresholds. All indices showed very broad limits of agreement. For example, the 95% confidence interval for diary and actigraphic total sleep time estimates fell within a 4.7-hour range.

CONCLUSIONS

Firefighters receiving recovery sleep between tours demonstrated significantly large disagreements between their daily self-reported sleep and measured actigraphic sleep. Sleep findings from actigraphic and Consensus Sleep Diary sleep assessments in this population should be interpreted cautiously until each method is compared against other reliable sleep analysis methods. Currently it is unclear if clinicians are using properly validated tools when diagnosing shift work disorder or other sleep disorders in firefighters.

CITATION

Marmis R, McGoldrick-Ruth L, Kelly MR, Haynes PL. Comparing actigraphy and diary to measure daily and average sleep in firefighters: a Bland-Altman analysis. J Clin Sleep Med. 2024;20(4):497-503.

Comparison between self-reported and actigraphy-derived sleep measures in patients receiving home parenteral nutrition: Secondary analysis of observational data

BACKGROUND

Patients receiving home parenteral nutrition (HPN) frequently report disrupted sleep. However, there are often inconsistencies between objectively measured and questionnaire-derived sleep measures. We compared sleep measures estimated from wrist actigraphy and self-report in adults receiving HPN.

METHODS

In this secondary analysis, we pooled data from two sleep-related studies enrolling adults receiving habitual HPN. We compared measures from 7-day averages of wrist actigraphy against comparable responses to a sleep questionnaire. Sleep measures included bedtime, wake time, time in bed, total sleep time, and sleep onset latency (SOL). Spearman correlation coefficients, Bland-Altman plots, and linear regression models for each set of sleep measures provided estimates of agreement.

RESULTS

Participants (N = 35) had a mean age of 52 years, body mass index of 21.6 kg/m2 , and 77% identified as female. Correlation coefficients ranged from 0.35 to 0.90, were highest for wake time (r = 0.90) and bedtime (r = 0.74), and lowest for total sleep time (r = 0.35). Actigraphy overestimated self-reported bedtime, wake time, and total sleep time and underestimated self-reported time in bed and SOL. Regression coefficients indicated the highest calibration for bedtime and wake time and lower calibration for time in bed, total sleep time, and SOL.

CONCLUSION

We observed strong-to-moderate agreement between sleep measures derived from wrist actigraphy and self-report in adults receiving HPN. Weaker correlations for total sleep time and SOL may indicate low wrist actigraphy sensitivity. Low-quality sleep resulting from sleep disruptions may have also contributed to an underreporting of perceived sleep quantity and lower concordance.

Methodological approach to sleep state misperception in insomnia disorder: Comparison between multiple nights of actigraphy recordings and a single night of polysomnography recording

STUDY OBJECTIVE

To provide a comprehensive assessment of sleep state misperception in insomnia disorder (INS) and good sleepers (GS) by comparing recordings performed for one night in-lab (PSG and night review) and during several nights at-home (actigraphy and sleep diaries).

METHODS

Fifty-seven INS and 29 GS wore an actigraphy device and filled a sleep diary for two weeks at-home. They subsequently completed a PSG recording and filled a night review in-lab. Sleep perception index (subjective/objective × 100) of sleep onset latency (SOL), sleep duration (TST) and wake duration (TST) were computed and compared between methods and groups.

RESULTS

GS displayed a tendency to overestimate TST and WASO but correctly perceived SOL. The degree of misperception was similar across methods within the GS group. In contrast, INS underestimated their TST and overestimated their SOL both in-lab and at-home, yet the severity of misperception of SOL was larger at-home than in-lab. Finally, INS overestimated WASO only in-lab while correctly perceiving it at-home. While only the degree of TST misperception was stable across methods in INS, misperception of SOL and WASO were dependent on the method used.

CONCLUSIONS

We found that GS and INS exhibit opposite patterns and severity of sleep misperception. While the degree of misperception in GS was similar across methods, only sleep duration misperception was reliably detected by both in-lab and at-home methods in INS. Our results highlight that, when assessing sleep misperception in insomnia disorder, the environment and method of data collection should be carefully considered.

Comparison of actigraphy with a sleep protocol maintained by professional caregivers and questionnaire-based parental judgment in children and adolescents with life-limiting conditions

BACKGROUND

Actigraphy offers a promising way to objectively assess pediatric sleep. Aim of the study was investigating the extent to which actigraphy used in children and adolescents with life-limiting conditions is consistent with two other measures of sleep diagnostics.

METHODS

In this monocentric prospective study N = 26 children and adolescents with life-limiting conditions treated on a pediatric palliative care unit were assessed. For three consecutive nights they wore an actigraph; the 24-hours sleep protocol documented by nurses and the Sleep Screening for Children and Adolescents with Complex Chronic Conditions (SCAC) answered by parents were analyzed. Patient characteristics and the parameters sleep onset, sleep offset, wake after sleep onset (WASO), number of wake phases, total sleep time (TST) and sleep efficiency (SE) were descriptively examined. Percentage bend correlations evaluated the three measures' concordance.

RESULTS

Descriptively, and except for the number of waking episodes, the different measures' estimations were comparable. Significant correlations existed between actigraphy and the sleep protocol for sleep onset (r = 0.83, p = < 0.001) and sleep offset (r = 0.89, p = < 0.001), between actigraphy and SCAC for SE (r = 0.59, p = 0.02).

CONCLUSION

Agreement of actigraphy with the focused sleep measures seems to be basically given but to varying degrees depending on the considered parameters.

Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample

Major Depressive Disorder (MDD) is a heterogeneous disorder, resulting in challenges with early detection. However, changes in sleep and movement patterns may help improve detection. Thus, this study aimed to explore the utility of wrist-worn actigraphy data in combination with machine learning (ML) and deep learning techniques to detect MDD using a commonly used screening method: Patient Health Questionnaire-9 (PHQ-9). Participants (N = 8,378; MDD Screening = 766 participants) completed the and wore Actigraph GT3X+ for one week as part of the National Health and Nutrition Examination Survey (NHANES). Leveraging minute-level, actigraphy data, we evaluated the efficacy of two commonly used ML approaches and identified actigraphy-derived biomarkers indicative of MDD. We employed two ML modeling strategies: (1) a traditional ML approach with theory-driven feature derivation, and (2) a deep learning Convolutional Neural Network (CNN) approach, coupled with gramian angular field transformation. Findings revealed movement-related features to be the most influential in the traditional ML approach and nighttime movement to be the most influential in the CNN approach for detecting MDD. Using a large, nationally-representative sample, this study highlights the potential of using passively-collected, actigraphy data for understanding MDD to better improve diagnosing and treating MDD.

Directional associations among real-time activity, sleep, mood, and daytime symptoms in major depressive disorder using actigraphy and ecological momentary assessment

Previous research has suggested that individuals with major depressive disorder (MDD) experienced alterations in sleep and activity levels. However, the temporal associations among sleep, activity levels, mood, and daytime symptoms in MDD have not been fully investigated. The present study aimed to fill this gap by utilizing real-time data collected across time points and days. 75 individuals with MDD and 75 age- and gender-matched healthy controls were recruited. Ecological momentary assessments (EMA) were adopted to assess real-time mood status for 7 days, and actigraphy was employed to measure day-to-day sleep-activity patterns. Multilevel modeling analyses were performed. Results revealed a bidirectional association between mood/daytime symptoms and activity levels across EMA intervals. Increased activity levels were predictive of higher alert cognition and positive mood, while an increase in positive mood also predicted more increase in activity levels in depressed individuals. A bidirectional association between sleep and daytime symptoms was also found. Alert cognition was found to be predictive of better sleep in the subsequent night. Contrariwise, higher sleep efficiency predicted improved alert cognition and sleepiness/fatigue the next day. A unidirectional association between sleep and activity levels suggested that higher daytime activity levels predicted a larger increase in sleep efficiency among depressed individuals. This study indicated how mood, activity levels, and sleep were temporally and intricately linked to each other in depressed individuals using actigraphy and EMA. It could pave the way for novel and efficacious treatments for depression that target not just mood but sleep and activity levels.

Comparison of actigraphy-measured and parent-reported sleep in association with weight status among preschool children

This study compared weekday and weekend actigraphy-measured and parent-reported sleep in relation to weight status among preschool-aged children. Participants were 3-6 years old preschoolers from the cross-sectional DAGIS-study with sleep data for ≥2 weekday and ≥2 weekend nights. Parents-reported sleep onset and wake-up times were gathered alongside 24 h hip-worn actigraphy. An unsupervised Hidden-Markov Model algorithm provided actigraphy-measured night time sleep without the guidance of reported sleep times. Waist-to-height ratio and age-and-sex-specific body mass index characterised weight status. Comparison of methods were assessed with consistency in quintile divisions and Spearman correlations. Associations between sleep and weight status were assessed with adjusted regression models. Participants included 638 children (49% girls) with a mean ± SD age of 4.76 ± 0.89. On weekdays, 98%-99% of actigraphy-measured and parent-reported sleep estimates were classified in the same or adjacent quintile and were strongly correlated (rs  = 0.79-0.85, p < 0.001). On weekends, 84%-98% of actigraphy-measured and parent-reported sleep estimates were respectively classified and correlations were moderate to strong (rs  = 0.62-0.86, p < 0.001). Compared with actigraphy-measured sleep, parent-reported sleep had consistently earlier onset, later wake-up, and greater duration. Earlier actigraphy-measured weekday sleep onset and midpoint were associated with a higher body mass index (respective β-estimates: -0.63, p < 0.01 and -0.75, p < 0.01) and waist-to-height ratio (-0.004, p = 0.03 and -0.01, p = 0.02). Though the sleep estimation methods were consistent and correlated, actigraphy measures should be favoured as they are more objective and sensitive to identifying associations between sleep timing and weight status compared with parent reports.

Use of Family Photographs Reduces Restlessness in Neurocritical Care Patients

ABSTRACT

BACKGROUND: Patients admitted to the neuroscience intensive care unit often experience varying states of confusion and restlessness. The purpose of this study was to examine restlessness in acutely confused patients through use of familiar photographs. METHODS : This randomized prospective pilot study placed family photographs (photos) on the bedrail of confused patients during the night shift (8 pm to 4 am ) in a neuroscience intensive care unit. Wrist actigraphy was used to examine restlessness when patients were turned to face the photos versus when they were not facing the photos. RESULTS: The 20 patients enrolled provided 34 nights worth of data during which 32 640 actigraph readings were obtained. On the first night of study, the odds of wrist movement were higher when the patient was facing the photos compared with not (odds ratio, 1.51; 95% confidence interval, 1.42-1.61). During subsequent nights, the odds of wrist movement were lower when the patient was facing the photos compared with not (odds ratio, 0.82; 95% confidence interval, 0.75-0.90). CONCLUSION : Use of familiar photos does not change restlessness, agitation, or delirium on the first night of observation. However, the use of familiar photos may decrease restlessness on the subsequent nights. There are important subjective observations from researchers and family that suggest all subjects had a noticeable response when initially seeing the familiar photos.

Evaluating Accuracy in Five Commercial Sleep-Tracking Devices Compared to Research-Grade Actigraphy and Polysomnography

The development of consumer sleep-tracking technologies has outpaced the scientific evaluation of their accuracy. In this study, five consumer sleep-tracking devices, research-grade actigraphy, and polysomnography were used simultaneously to monitor the overnight sleep of fifty-three young adults in the lab for one night. Biases and limits of agreement were assessed to determine how sleep stage estimates for each device and research-grade actigraphy differed from polysomnography-derived measures. Every device, except the Garmin Vivosmart, was able to estimate total sleep time comparably to research-grade actigraphy. All devices overestimated nights with shorter wake times and underestimated nights with longer wake times. For light sleep, absolute bias was low for the Fitbit Inspire and Fitbit Versa. The Withings Mat and Garmin Vivosmart overestimated shorter light sleep and underestimated longer light sleep. The Oura Ring underestimated light sleep of any duration. For deep sleep, bias was low for the Withings Mat and Garmin Vivosmart while other devices overestimated shorter and underestimated longer times. For REM sleep, bias was low for all devices. Taken together, these results suggest that proportional bias patterns in consumer sleep-tracking technologies are prevalent and could have important implications for their overall accuracy.

The middle-aged and older Chinese adults' health using actigraphy in Taiwan (MOCHA-T): protocol for a multidimensional dataset of health and lifestyle

BACKGROUND AND OBJECTIVES

Older adults keep transforming with Baby Boomers and Gen Xers being the leading older population. Their lifestyle, however, is not well understood. The middle-aged and older Chinese adults' health using actigraphy in Taiwan (MOCHA-T) collected both objective and subjective data to depict the health and lifestyle of this population. The objectives, design, and measures of the MOCHA-T study are introduced, and the caveats and future directions related to the use of the data are presented.

METHODS

People aged 50 and over were recruited from the community, with a subset of women aged 45-49 invited to supplement data on menopause and aging. Four instruments (i.e., self-reported questionnaires, diary, wrist actigraphy recorder, and GPS) were used to collect measures of sociodemographic, health, psychosocial, behavioral, temporal, and spatial data.

RESULTS

A total of 242 participants who returned the informed consent and questionnaires were recruited in the MOCHA-T study. Among them, 94.6%, 95.0%, and 25.2% also completed the diary, actigraphy, and GPS data, respectively. There was almost no difference in sociodemographic characteristics between those with and without a completed diary, actigraphy, and GPS data, except for age group and educational level for those who returned completed actigraphy data.

CONCLUSION

The MOCHA-T study is a multidimensional dataset that allows researchers to describe the health, behaviors, and lifestyle patterns, and their interactions with the environment of the newer generation of middle-aged and older adults in Taiwan. It can be compared with other countries with actigraphy and GPS-based lifestyle data of middle-aged and older adults in the future.

Effects of ActiGraph's filter, epoch length and non-wearing time algorithm on step counts in people with COPD

The influence of the ActiGraph® processing criteria on estimating step counts in chronic obstructive pulmonary disease (COPD) remains uncertain. This study aimed to assess the influence of filters, epoch lengths and non-wearing time (NWT) algorithms on steps/day in people with COPD. ActiGraph GT3X+ was worn on the waist for seven days. Steps were detected using different filters (normal and low-frequency extension [LFE]), epoch lengths (15s and 60s), and NWT algorithms (Choi and Troiano). Linear mixed-effects model was applied to assess the effects of filter, epoch length, NWT algorithm on steps/day. Lin's concordance correlation and Bland-Altman were used to measure agreement. A total of 136 people with COPD (107 male; 69 ± 8 years; FEV1 51 ± 17% predicted) were included. Significant differences were found between filters (p < 0.001), but not between epoch lengths or NWT algorithms. The LFE increased, on average, approximately 7500 steps/day compared to the normal filter (p < 0.001). Agreement was poor (<0.3) and proportional bias was significant when comparing steps/day computed with different filters, regardless of the epoch length and NWT algorithm. Filter choice but not epoch lengths or NWT algorithms seem to impact measurement of steps/day. Future studies are needed to recommend the most accurate technique for measuring steps/day in people with COPD.

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

BACKGROUND

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.

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Six multidimensional sleep health facets in older adults identified with factor analysis of actigraphy: Results from the Einstein Aging Study

OBJECTIVES

The concept of multi-dimensional sleep health, originally based on self-report, was recently extended to actigraphy in older adults, yielding five components, but without a hypothesized rhythmicity factor. The current study extends prior work using a sample of older adults with a longer period of actigraphy follow-up, which may facilitate observation of the rhythmicity factor.

METHODS

Wrist actigraphy measures of participants (N = 289, Mage = 77.2 years, 67% females; 47% White, 40% Black, 13% Hispanic/Others) over 2 weeks were used in exploratory factor analysis to determine factor structures, followed by confirmatory factor analysis on a different subsample. The utility of this approach was demonstrated by associations with global cognitive performance (Montreal Cognitive Assessment).

RESULTS

Exploratory factor analysis identified six factors: Regularity: standard deviations of four sleep measures: midpoint, sleep onset time, night total sleep time (TST), and 24-hour TST; Alertness/Sleepiness (daytime): amplitude, napping (mins and #/day); Timing: sleep onset, midpoint, wake-time (of nighttime sleep); up-mesor, acrophase, down-mesor; Efficiency: sleep maintenance efficiency, wake after sleep onset; Duration: night rest interval(s), night TST, 24-hour rest interval(s), 24-hour TST; Rhythmicity (pattern across days): mesor, alpha, and minimum. Greater sleep efficiency was associated with better Montreal Cognitive Assessment performance (β [95% confidence interval] = 0.63 [0.19, 1.08]).

CONCLUSIONS

Actigraphic records over 2 weeks revealed that Rhythmicity may be an independent factor in sleep health. Facets of sleep health can facilitate dimension reduction, be considered predictors of health outcomes, and be potential targets for sleep interventions.

A comparative study of different sleep assessment methods for preschool children

PURPOSE

This study aimed to examine the differences between different sleep assessment methods for preschool children.

METHODS

Preschool children (n = 54, mean age: 4.6 years) were recruited from kindergarten. Data were collected using an accelerometer, a sleep log, and sleep questionnaire. Furthermore, correlation analysis, Bland-Altman analysis, and repeated measures ANOVA were performed.

RESULTS

(1) The sleep duration evaluated by different sleep assessment methods were all significantly correlated, among which the sleep log and Sadeh algorithm showed the highest correlation (r = 0.972, p < .001), while the Tudor-Locke algorithm and the sleep questionnaire demonstrated the lowest correlation (r = 0.383, p < .01); (2) The points between different sleep assessment methods were all within 95% LoA, except for the sleep log and Tudor-Locke algorithm; (3) In various methods of sleep assessment, significant differences were observed in sleep onset (F2 (1.6,85.0)  = 32.8, p < .001, η2 : 0.38), while no significant differences were observed in sleep offset (F2 (1.5,80.1)  = 32.8, p = .05, η2 : 0.05); (4) In addition, no significant difference in sleep onset was observed between the sleep questionnaire and sleep log (p > .05), and there was also no significant difference in sleep onset between the Sadeh algorithm and the Tudor-Locke algorithm (p > .05).

CONCLUSIONS

Both the Sadeh algorithm and the Tudor-Locke algorithm can be used as effective algorithms for sleep duration assessment of Chinese preschool children, with the latter having obvious advantages in large sample surveys. Future research should pay attention to the differences between different sleep assessment methods when using these algorithms.

Associations between actigraphy-derived rest-activity rhythm characteristics and hypertension in United States adults

People with disrupted circadian rhythms, such as shift workers, have shown a higher risk of hypertension. However, it is unclear whether more subtle differences in diurnal rest-activity rhythms in the population are associated with hypertension. Clarifying the association between the rest-activity rhythm, a modifiable behavioural factor, and hypertension could provide insight into preventing hypertension and possibly cardiovascular diseases. In this study, we investigated the association between rest-activity rhythm characteristics and hypertension in a large representative sample of United States adults. Cross-sectional data were obtained from the National Health and Nutrition Examination Survey 2011-2014 (N = 6726; mean [range] age 49 [20-79] years; 52% women). Five rest-activity rhythm parameters (i.e., pseudo F statistic, amplitude, mesor, amplitude:mesor ratio, and acrophase) were derived from 24-h actigraphy data using the extended cosine model. We performed multiple logistic regression to assess the associations between the rest-activity rhythm parameters and hypertension. Subgroup analysis stratified by age, gender, race/ethnicity, body mass index and diabetes status was also conducted. A weakened overall rest-activity rhythm, characterised by a lower F statistic, was associated with higher odds of hypertension (odds ratio quintile 1 versus quintile 5 [OR Q1vs.Q5 ] 1.61, 95% confidence interval [CI] 1.26-2.05; p trend < 0.001). Similar results were found for lower amplitude (OR Q1vs.Q5 1.51, 95% CI 1.13-2.03; p trend = 0.01) and amplitude:mesor ratio (OR Q1vs.Q5 1.34, 95% CI 1.01-1.78; p trend = 0.03). The results were robust to the adjustment of confounders, individual behaviours including physical activity levels and sleep duration and appeared consistent across subgroups. Possible interaction between the rest-activity rhythm and body mass index was found. Our results support an association between weakened rest-activity rhythms and higher odds of hypertension.

Automatic sleep-stage classification of heart rate and actigraphy data using deep and transfer learning approaches

Manual sleep-stage scoring based on full-night polysomnography data recorded in a sleep lab has been the gold standard of clinical sleep medicine. This costly and time-consuming approach is unfit for long-term studies as well as assessment of sleep on a population level. With the vast amount of physiological data becoming available from wrist-worn devices, deep learning techniques provide an opportunity for fast and reliable automatic sleep-stage classification tasks. However, training a deep neural network requires large annotated sleep databases, which are not available for long-term epidemiological studies. In this paper, we introduce an end-to-end temporal convolutional neural network able to automatically score sleep stages from raw heartbeat RR interval (RRI) and wrist actigraphy data. Moreover, a transfer learning approach enables the training of the network on a large public database (Sleep Heart Health Study, SHHS) and its subsequent application to a much smaller database recorded by a wristband device. The transfer learning significantly shortens training time and improves sleep-scoring accuracy from 68.9% to 73.8% and inter-rater reliability (Cohen's kappa) from 0.51 to 0.59. We also found that for the SHHS database, automatic sleep-scoring accuracy using deep learning shows a logarithmic relationship with the training size. Although deep learning approaches for automatic sleep scoring are not yet comparable to the inter-rater reliability among sleep technicians, performance is expected to significantly improve in the near future when more large public databases become available. We anticipate those deep learning techniques, when combined with our transfer learning approach, will leverage automatic sleep scoring of physiological data from wearable devices and enable the investigation of sleep in large cohort studies.

Performance evaluation of Fitbit Charge 3 and actigraphy vs. polysomnography: Sensitivity, specificity, and reliability across participants and nights

GOAL AND AIMS

Compare the accuracy and reliability of sleep/wake classification between the Fitbit Charge 3 and the Micro Motionlogger actigraph when applying either the Cole-Kripke or Sadeh scoring algorithms. Accuracy was established relative to simultaneous Polysomnography recording. Focus technology: Fitbit Charge 3 and actigraphy. Reference technology: Polysomnography.

SAMPLE

Twenty-one university students (10 females).

DESIGN

Simultaneous Fitbit Charge 3, actigraphy, and polysomnography were recorded over 3 nights at the participants' homes.

CORE ANALYTICS

Total sleep time, wake after sleep onset, sensitivity, specificity, positive predictive value, and negative predictive value.

ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES

Variability of specificity and negative predictive value across subjects and across nights.

CORE OUTCOMES

Fitbit Charge 3 and actigraphy using the Cole-Kripke or Sadeh algorithms exhibited similar sensitivity in classifying sleep segments relative to polysomnography (sensitivity of 0.95, 0.96, and 0.95, respectively). Fitbit Charge 3 was significantly more accurate in classifying wake segments (specificity of 0.69, 0.33, and 0.29, respectively). Fitbit Charge 3 also exhibited significantly higher positive predictive value than actigraphy (0.99 vs. 0.97 and 0.97, respectively) and a negative predictive value that was significantly higher only relative to the Sadeh algorithm (0.41 vs. 0.25, respectively).

IMPORTANT ADDITIONAL OUTCOMES

Fitbit Charge 3 exhibited significantly lower standard deviation in specificity values across subjects and negative predictive value across nights.

CORE CONCLUSION

This study demonstrates that Fitbit Charge 3 is more accurate and reliable in identifying wake segments than the examined FDA-approved Micro Motionlogger actigraphy device. The results also highlight the need to create devices that record and save raw multi-sensor data, which are necessary for developing open-source sleep or wake classification algorithms.

Association between circadian skin temperature rhythms and actigraphic sleep measures in real-life settings

STUDY OBJECTIVES

Skin temperature manipulation with little or no change in core body temperature affects sleep-wake states; however, the association of 24-hour skin temperature variation with sleep quality has not been investigated in a large-scale population. We examined the association between the circadian rhythm of distal skin temperature and sleep quality in real-life settings and aimed to provide additional evidence of the link between thermoregulation and sleep-wake states.

METHODS

In this cross-sectional study of 2,187 community-dwelling adults, we measured distal skin temperature at the ventral forearm at 3-minute intervals for 7 consecutive days to calculate nonparametric indicators of a circadian skin temperature rhythm, including intradaily variability, interdaily stability, and relative amplitude. Participants underwent simultaneous 7-day wrist actigraphy to objectively measure sleep quality. The association between nonparametric circadian skin temperature rhythm indicators and 7-day sleep measures was evaluated using multivariable linear regression models.

RESULTS

Lower intradaily variability and higher interdaily stability and relative amplitude of distal skin temperature were significantly associated with higher sleep efficiency, shorter wake after sleep onset, and longer total sleep time (all P < .001). After adjusting for demographic, clinical, and environmental factors, the coefficients for the linear trend of sleep efficiency were -1.20 (95% confidence interval: -1.53, -0.87), 1.08 (95% confidence interval: 0.80-1.36), and 1.47 (95% confidence interval: 1.04-1.89) per quartile increase in intradaily variability, interdaily stability, and relative amplitude, respectively (all P < .001).

CONCLUSIONS

Distal skin temperature with lower fluctuations and higher regularity and rhythm amplitudes was associated with better sleep quality. Our results could be applied in chronobiological interventions to improve sleep quality.

CITATION

Tai Y, Obayashi K, Yamagami Y, Saeki K. Association between circadian skin temperature rhythms and actigraphic sleep measures in real-life settings. J Clin Sleep Med. 2023;19(7):1281-1292.

Does Sleep Quality Influence Recovery Outcomes After Postconcussive Injury in Children and Adolescents?

OBJECTIVE

To determine whether objective parameters of sleep quality differ throughout recovery between children and adolescents who experienced an early return to school (RTS) and those who had a delayed RTS or did not return at all during the study period.

SETTING

Sleep parameters reflective of sleep quality were evaluated in participants' natural sleeping habitat throughout 9 weeks postinjury.

PARTICIPANTS

Ninety-four children and adolescents (aged 5-18 years) with diagnosed concussion.

DESIGN

Prospective cohort. Participants followed RTS protocols.

MAIN MEASURES

Actigraphy-derived estimates of total sleep time (TST), sleep efficiency (SE), wake after sleep onset (WASO), average arousal length (AAL), and number of arousals (NOAs) per hour were assessed. The length of time from injury until RTS was determined for each participant. Participants were categorized into an early RTS or delayed RTS group based on their time to RTS.

RESULTS

Both TST and SE were significantly greater in the early RTS group. WASO duration, AAL, and NOAs were significantly greater in the delayed RTS group. Differences between RTS groups were most apparent during weeks 1 to 5 postinjury.

CONCLUSIONS AND CLINICAL IMPLICATIONS

Participants who returned to school earlier had significantly better objective sleep quality than participants who experienced a delayed RTS. This study provides evidence in support of a relationship between sleep quality and time to RTS in children and adolescents with concussion. Considering early monitoring of sleep, education regarding sleep hygiene, and access to age-appropriate sleep interventions may be helpful in pediatric concussion recovery.

Development and performance of a sleep estimation algorithm using a single accelerometer placed on the thigh: an evaluation against polysomnography

Accelerometers placed on the thigh provide accurate measures of daily physical activity types, postures and sedentary behaviours, over 24 h and across consecutive days. However, the ability to estimate sleep duration or quality from thigh-worn accelerometers is uncertain and has not been evaluated in comparison with the 'gold-standard' measurement of sleep polysomnography. This study aimed to develop an algorithm for sleep estimation using the raw data from a thigh-worn accelerometer and to evaluate it in comparison with polysomnography. The algorithm was developed and optimised on a dataset consisting of 23 single-night polysomnography recordings, collected in a laboratory, from 15 asymptomatic adults. This optimised algorithm was then applied to a separate evaluation dataset, in which, 71 adult males (mean [SD] age 57 [11] years, height 181 [6] cm, weight 82 [13] kg) wore ambulatory polysomnography equipment and a thigh-worn accelerometer, simultaneously, whilst sleeping at home. Compared with polysomnography, the algorithm had a sensitivity of 0.84 and a specificity of 0.55 when estimating sleep periods. Sleep intervals were underestimated by 21 min (130 min, Limits of Agreement Range [LoAR]). Total sleep time was underestimated by 32 min (233 min LoAR). Our results evaluate the performance of a new algorithm for estimating sleep and outline the limitations. Based on these results, we conclude that a single device can provide estimates of the sleep interval and total sleep time with sufficient accuracy for the measurement of daily physical activity, sedentary behaviour, and sleep, on a group level in free-living settings.

24-Hour Rest-Activity Rhythm in Middle-Aged and Older Persons with Depression

Changes in rest or active states were clinically observed in persons with depression. However, the association between symptoms of depression and 24 h rest-activity rhythm (RAR) components that can be measured using wearable devices was not clarified. This preliminary cross-sectional study aimed to clarify the 24 h RAR components associated with symptoms of depression in middle-aged and older persons. Participants were recruited from among inpatients and outpatients requiring medical treatment at Akita University Hospital for the group with depression and from among healthy volunteers living in Akita prefecture, Japan, for the healthy control group. To assess RAR parameters including inter-daily stability (IS), intra-daily variability (IV), relative amplitude (RA), and average physical activity level for the most active 10 h span (M10) or for the least active 5 h span (L5), all the participants were instructed to wear an Actiwatch Spectrum Plus device on their non-dominant wrist for seven days. Twenty-nine persons with depression and 30 controls were included in the analysis. The results of a binomial regression analysis showed that symptoms of depression were significantly associated with a high IS value (odds ratio [OR], 1.20; 95% confidence interval [95% CI], 1.01-1.44; p = 0.04) and a low M10 value (OR, 0.85; 95% CI, 0.74-0.96; p = 0.01). Our findings suggest potential components of 24 h RAR are associated with depression.

Sleep variability over a 2-week period is associated with restfulness and intrinsic limbic network connectivity in adolescents

STUDY OBJECTIVES

Sleep duration and intraindividual variability in sleep duration undergo substantial changes in adolescence and impact brain and behavioral functioning. Although experimental work has linked acute sleep deprivation to heightened limbic responding and reduced regulatory control, there is limited understanding of how variability in sleep patterns might interact with sleep duration to influence adolescent functioning. This is important for optimal balancing of length and consistency of sleep. Here, we investigated how objective indices of sleep duration and variability relate to stress, restfulness, and intrinsic limbic network functioning in adolescents.

METHODS

A sample of 101 adolescents ages 14-18 reported their stressors, after which they wore wrist actigraph watches to monitor their sleep and rated their restfulness every morning over a 2-week period. They also completed a resting-state fMRI scan.

RESULTS

Adolescents reporting more stress experienced shorter sleep duration and greater sleep variability over the 2-week period. Longer nightly sleep duration was linked to feeling more rested the next morning, but this effect was reduced in adolescents with high cumulative sleep variability. Sleep variability showed both linear and quadratic effects on limbic connectivity: adolescents with high sleep variability exhibited more connectivity within the limbic network and less connectivity between the limbic and frontoparietal networks than their peers, effects which became stronger once variability exceeded an hour.

CONCLUSIONS

Results suggest that cumulative sleep variability is related to stress and limbic network connectivity and shows interactive effects with sleep duration, highlighting the importance of balancing length and consistency of sleep for optimal functioning in adolescence.

The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder

(1) Background and Goal: Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of our study was to examine actigraphic measures to identify features that can be extracted from them so that a machine learning model can detect premorbid latent liabilities for schizotypy and bipolarity. (2) Methods: Our team developed a small wrist-worn measurement device that collects and identifies actigraphic data based on an accelerometer. The sensors were used by carefully selected healthy participants who were divided into three groups: Control Group (C), Cyclothymia Factor Group (CFG), and Positive Schizotypy Factor Group (PSF). From the data they collected, our team performed data cleaning operations and then used the extracted metrics to generate the feature combinations deemed most effective, along with three machine learning algorithms for categorization. (3) Results: By conducting the training, we were able to identify a set of mildly correlated traits and their order of importance based on the Shapley value that had the greatest impact on the detection of bipolarity and schizotypy according to the logistic regression, Light Gradient Boost, and Random Forest algorithms. (4) Conclusions: These results were successfully compared to the results of other researchers; we had a similar differentiation in features used by others, and successfully developed new ones that might be a good complement for further research. In the future, identifying these traits may help us identify people at risk from mental disorders early in a cost-effective, automated way.

Agreement between actigraphy and sleep diaries: A 28-day real-time monitoring study among suicidal adolescents following acute psychiatric care

OBJECTIVE

To examine the agreement between, and adherence to, wrist actigraphy and digital sleep diaries as methods for sleep assessment among high-risk adolescents in the 28 days following discharge from acute psychiatric care. Sleep parameters included: number of nighttime awakenings (NWAK), sleep efficiency (SE), sleep onset latency (SOL), total sleep time (TST), and wake after sleep onset (WASO).

METHODS

Fifty-three adolescents (12-18 years) were recruited following discharge from acute psychiatric care for suicide risk. Adolescents completed a baseline assessment followed by a 28-day monitoring period with daily sleep diaries and continuous wrist actigraphy. Bland-Altman and multi-level models examined agreement.

RESULTS

Adherence to actigraphy was high, but lower for sleep diaries; a similar pattern of adherence emerged on weekdays vs. weekends. Bland-Altman analyses revealed no clinically meaningful bias for sleep parameters (except NWAK), but the limits of agreement make interpretation ambiguous. Our base model indicated strong agreement between actigraphy and sleep diaries for TST (r = 0.850), moderate for SOL (r = 0.325) and SE (r = 0.322), and weak for WASO (r = -0.049) and NWAK (r = 0.114). A similar pattern emerged with the insomnia severity models with baseline insomnia influencing agreement on all parameters. There were significant weekday-weekend differences for WASO and NWAK, but not for SOL, SE, and TST.

CONCLUSION

Results suggest that it may be beneficial to find a modeling approach to account for the concordant and discordant information and relevant time-level variables.

Naturalistic Sleep Patterns are Linked to Global Structural Brain Aging in Adolescence

PURPOSE

We examined whether interindividual differences in naturalistic sleep patterns correlate with any deviations from typical brain aging.

METHODS

Our sample consisted of 251 participants without current psychiatric diagnoses (9-25 years; mean [standard deviation] = 17.4 ± 4.52 yr; 58% female) drawn from the Neuroimaging and Pediatric Sleep Databank. Participants completed a T1-weighted structural magnetic resonance imaging scan and 5-7 days of wrist actigraphy to assess naturalistic sleep patterns (duration, timing, continuity, and regularity). We estimated brain age from extracted structural magnetic resonance imaging indices and calculated brain age gap (estimated brain age-chronological age). Robust regressions tested cross-sectional associations between brain age gap and sleep patterns. Exploratory models investigated moderating effects of age and biological gender and, in a subset of the sample, links between sleep, brain age gap, and depression severity (Patient-Reported Outcomes Measurement Information System Depression).

RESULTS

Later sleep timing (midsleep) was associated with more advanced brain aging (larger brain age gap), β = 0.1575, puncorr = .0042, pfdr = .0167. Exploratory models suggested that this effect may be driven by males, although the interaction of gender and brain age gap did not survive multiple comparison correction (β = 0.2459, puncorr = .0336, pfdr = .1061). Sleep duration, continuity, and regularity were not significantly associated with brain age gap. Age did not moderate any brain age gap-sleep relationships. In this psychiatrically healthy sample, depression severity was also not associated with brain age gap or sleep.

DISCUSSION

Later midsleep may be one behavioral cause or correlate of more advanced brain aging, particularly among males. Future studies should examine whether advanced brain aging and individual differences in sleep precede the onset of suboptimal cognitive-emotional outcomes in adolescents.

Exploring Predictors of Sleep State Misperception in Women with Posttraumatic Stress Disorder

OBJECTIVES

Insomnia is a common symptom of posttraumatic stress disorder (PTSD) that is resistant to first-line cognitive behavioral interventions. However, research suggests that, among individuals with PTSD, self-reported sleep impairment is typically more severe than what is objectively observed, a phenomenon termed sleep state misperception. Relatively little research has examined which individuals with PTSD are most likely to exhibit sleep state misperception. This study explored clinical predictors of sleep state misperception in a sample of 43 women with PTSD and clinically significant sleep impairment.

METHOD

During a baseline assessment, participants' PTSD symptoms were assessed using a clinical interview and their sleep was assessed using the Pittsburgh Sleep Quality Index (PSQI). Objective sleep, self-reported sleep, and PTSD symptoms were then assessed over a 1-week period using actigraphy and daily diaries.

RESULTS

Consistent with previous research, women in the study exhibited total sleep time (TST), sleep efficiency (SE), and sleep onset latency (SOL) sleep state misperception. For TST and SE, but not SOL, discrepancies between actigraphy and the PSQI were associated with each clinician-rated PTSD symptom cluster, whereas discrepancies between actigraphy and daily diary were only associated with clinician-rated reexperiencing symptoms. The only self-reported PTSD symptom that was uniquely associated with sleep state misperception was nightmares. This association was no longer significant after controlling for sleep-related anxiety.

CONCLUSIONS

Results suggest that women with more severe reexperiencing symptoms of PTSD, particularly nightmares, may be more likely to exhibit TST and SE sleep state misperception, perhaps due to associated sleep-related anxiety.

Validity of Two Consumer Multisport Activity Tracker and One Accelerometer against Polysomnography for Measuring Sleep Parameters and Vital Data in a Laboratory Setting in Sleep Patients

Two commercial multisport activity trackers (Garmin Forerunner 945 and Polar Ignite) and the accelerometer ActiGraph GT9X were evaluated in measuring vital data, sleep stages and sleep/wake patterns against polysomnography (PSG). Forty-nine adult patients with suspected sleep disorders (30 males/19 females) completed a one-night PSG sleep examination followed by a multiple sleep latency test (MSLT). Sleep parameters, time in bed (TIB), total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), awake time (WASO + SOL), sleep stages (light, deep, REM sleep) and the number of sleep cycles were compared. Both commercial trackers showed high accuracy in measuring vital data (HR, HRV, SpO2, respiratory rate), r > 0.92. For TIB and TST, all three trackers showed medium to high correlation, r > 0.42. Garmin had significant overestimation of TST, with MAE of 84.63 min and MAPE of 25.32%. Polar also had an overestimation of TST, with MAE of 45.08 min and MAPE of 13.80%. ActiGraph GT9X results were inconspicuous. The trackers significantly underestimated awake times (WASO + SOL) with weak correlation, r = 0.11−0.57. The highest MAE was 50.35 min and the highest MAPE was 83.02% for WASO for Garmin and ActiGraph GT9X; Polar had the highest MAE of 21.17 min and the highest MAPE of 141.61% for SOL. Garmin showed significant deviations for sleep stages (p < 0.045), while Polar only showed significant deviations for sleep cycle (p = 0.000), r < 0.50. Garmin and Polar overestimated light sleep and underestimated deep sleep, Garmin significantly, with MAE up to 64.94 min and MAPE up to 116.50%. Both commercial trackers Garmin and Polar did not detect any daytime sleep at all during the MSLT test. The use of the multisport activity trackers for sleep analysis can only be recommended for general daily use and for research purposes. If precise data on sleep stages and parameters are required, their use is limited. The accuracy of the vital data measurement was adequate. Further studies are needed to evaluate their use for medical purposes, inside and outside of the sleep laboratory. The accelerometer ActiGraph GT9X showed overall suitable accuracy in detecting sleep/wake patterns.

Smartphone Pedometer Sensor Application for Evaluating Disease Activity and Predicting Comorbidities in Patients with Rheumatoid Arthritis: A Validation Study

Smartphone-based pedometer sensor telemedicine applications could be useful for measuring disease activity and predicting the risk of developing comorbidities, such as pulmonary or cardiovascular disease, in patients with rheumatoid arthritis (RA), but the sensors have not been validated in this patient population. The aim of this study was to validate step counting with an activity-tracking application running the inbuilt Android smartphone pedometer virtual sensor in patients with RA. Two Android-based smartphones were tested in a treadmill test-bed setup at six walking speeds and compared to manual step counting as the gold standard. Guided by a facilitator, the participants walked 100 steps at each test speed, from 2.5 km/h to 5 km/h, wearing both devices simultaneously in a stomach pouch. A computer automatically recorded both the manually observed and the sensor step count. The overall difference in device step counts versus the observed was 5.9% mean absolute percentage error. Highest mean error was at the 2.5 km/h speed tests, where the mean error of the two devices was 18.5%. Both speed and cadence were negatively correlated to the absolute percentage error, which indicates that the greater the speed and cadence, the lower the resulting step counting error rate. There was no correlation between clinical parameters and absolute percentage error. In conclusion, the activity-tracking application using the inbuilt Android smartphone pedometer virtual sensor is valid for step counting in patients with RA. However, walking at very low speed and cadence may represent a challenge.

Effects of methadone, buprenorphine, and naltrexone on actigraphy-based sleep-like parameters in male rhesus monkeys

Opioid use disorder (OUD) has been associated with the emergence of sleep disturbances. Although effective treatments for OUD exist, evidence suggests that these treatments also may be associated with sleep impairment. The extent to which these effects are an effect of OUD treatment or a result of chronic opioid use remains unknown. We investigated the acute effects of methadone, buprenorphine, and naltrexone on actigraphy-based sleep-like parameters in non-opioid-dependent male rhesus monkeys (Macaca mulatta, n = 5). Subjects were fitted with actigraphy monitors attached to primate collars to measure sleep-like parameters. Actigraphy recordings were conducted under baseline conditions, or following acute injections of vehicle, methadone (0.03-1.0 mg/kg, i.m.), buprenorphine (0.01-1.0 mg/kg, i.m.), or naltrexone (0.03-1.0 mg/kg, i.m.) in the morning (4 h after "lights on") or in the evening (1.5 h before "lights off"). Morning and evening treatments with methadone or buprenorphine significantly increased sleep latency and decreased sleep efficiency. The effects of buprenorphine on sleep-like measures resulted in a biphasic dose-response function, with the highest doses not disrupting actigraphy-based sleep. Buprenorphine induced a much more robust increase in sleep latency and decrease in sleep efficiency compared to methadone, particularly with evening administration, and detrimental effects of buprenorphine on sleep-like measures were observed up to 25.5 h after drug injection. Treatment with naltrexone, on the other hand, significantly improved sleep-like measures, with evening treatments improving both sleep latency and sleep efficiency. The currently available pharmacotherapies for OUD significantly alter sleep-like parameters in non-opioid-dependent monkeys, and opioid-dependent mechanisms may play a significant role in sleep-wake regulation.

A comparison of agreement between actigraphy and polysomnography for assessing sleep during posttraumatic amnesia

STUDY OBJECTIVES

Sleep disturbance often emerges in the early recovery phase following a moderate to severe traumatic brain injury, known as posttraumatic amnesia. Actigraphy is commonly employed to assess sleep, as it is assumed that patients in posttraumatic amnesia (who display confusion, restlessness, and agitation) would better tolerate this measure over gold-standard polysomnography (PSG). This study evaluated the agreement between PSG and actigraphy for determining (sleep/wake time, sleep efficiency, sleep latency, and awakenings) in patients experiencing posttraumatic amnesia. It also compared the epoch-by-epoch sensitivity, specificity, and accuracy between the Actigraph device's 4 wake threshold settings (low, medium, high, and automatic) to PSG.

METHODS

The sample consisted of 24 inpatients recruited from a traumatic brain injury inpatient rehabilitation unit. Ambulatory PSG was recorded overnight at bedside and a Philips Actiwatch was secured to each patient's wrist for the same period.

RESULTS

There were poor correlations between PSG and actigraphy for all parameters (Lin's concordance correlation coefficient = < 0.80). The low threshold displayed the highest correlation with PSG for wake and sleep time, albeit still low. Actigraphy displayed low specificity (ranging from 17.1% to 36.6%). There appears to be a greater disparity between actigraphy and PSG for patients with increased wake time.

CONCLUSIONS

Actigraphy, while convenient, demonstrated poorer performance in determining sleep-wake parameters in patients with significantly disturbed sleep. Ambulatory PSG can provide a clearer understanding of the extent of sleep disturbances in these patients with reduced mobility during early rehabilitation. Study findings can help design future protocols of sleep assessment during posttraumatic amnesia and optimize treatment.

CITATION

Fedele B, McKenzie D, Williams G, Giles R, Olver J. A comparison of agreement between actigraphy and polysomnography for assessing sleep during posttraumatic amnesia. J Clin Sleep Med. 2022;18(11):2605-2616.

Daytime rest: Association with 24-h rest-activity cycles, circadian timing and cognition in older adults

Growing epidemiological evidence points toward an association between fragmented 24-h rest-activity cycles and cognition in the aged. Alterations in the circadian timing system might at least partially account for these observations. Here, we tested whether daytime rest (DTR) is associated with changes in concomitant 24-h rest probability profiles, circadian timing and neurobehavioural outcomes in healthy older adults. Sixty-three individuals (59-82 years) underwent field actigraphy monitoring, in-lab dim light melatonin onset assessment and an extensive cognitive test battery. Actimetry recordings were used to measure DTR frequency, duration and timing and to extract 24-h rest probability profiles. As expected, increasing DTR frequency was associated not only with higher rest probabilities during the day, but also with lower rest probabilities during the night, suggesting more fragmented night-time rest. Higher DTR frequency was also associated with lower episodic memory performance. Moreover, later DTR timing went along with an advanced circadian phase as well as with an altered phase angle of entrainment between the rest-activity cycle and circadian phase. Our results suggest that different DTR characteristics, as reflective indices of wake fragmentation, are not only underlined by functional consequences on cognition, but also by circadian alteration in the aged.

Modeling Sleep Quality Depending on Objective Actigraphic Indicators Based on Machine Learning Methods

According to data from the World Health Organization and medical research centers, the frequency and severity of various sleep disorders, including insomnia, are increasing steadily. This dynamic is associated with increased daily stress, anxiety, and depressive disorders. Poor sleep quality affects people's productivity and activity and their perception of quality of life in general. Therefore, predicting and classifying sleep quality is vital to improving the quality and duration of human life. This study offers a model for assessing sleep quality based on the indications of an actigraph, which was used by 22 participants in the experiment for 24 h. Objective indicators of the actigraph include the amount of time spent in bed, sleep duration, number of awakenings, and duration of awakenings. The resulting classification model was evaluated using several machine learning methods and showed a satisfactory accuracy of approximately 80-86%. The results of this study can be used to treat sleep disorders, develop and design new systems to assess and track sleep quality, and improve existing electronic devices and sensors.

Sleep Measurement Using Wrist-Worn Accelerometer Data Compared with Polysomnography

This study determined if using alternative sleep onset (SO) definitions impacted accelerometer-derived sleep estimates compared with polysomnography (PSG). Nineteen participants (48%F) completed a 48 h visit in a home simulation laboratory. Sleep characteristics were calculated from the second night by PSG and a wrist-worn ActiGraph GT3X+ (AG). Criterion sleep measures included PSG-derived Total Sleep Time (TST), Sleep Onset Latency (SOL), Wake After Sleep Onset (WASO), Sleep Efficiency (SE), and Efficiency Once Asleep (SE_ASLEEP). Analogous variables were derived from temporally aligned AG data using the Cole-Kripke algorithm. For PSG, SO was defined as the first score of 'sleep'. For AG, SO was defined three ways: 1-, 5-, and 10-consecutive minutes of 'sleep'. Agreement statistics and linear mixed effects regression models were used to analyze 'Device' and 'Sleep Onset Rule' main effects and interactions. Sleep-wake agreement and sensitivity for all AG methods were high (89.0-89.5% and 97.2%, respectively); specificity was low (23.6-25.1%). There were no significant interactions or main effects of 'Sleep Onset Rule' for any variable. The AG underestimated SOL (19.7 min) and WASO (6.5 min), and overestimated TST (26.2 min), SE (6.5%), and SE_ASLEEP (1.9%). Future research should focus on developing sleep-wake detection algorithms and incorporating biometric signals (e.g., heart rate).

Automated classification of sleep and wake from single day triaxial accelerometer data

Actigraphy allows for the remote monitoring of subjects' activity for clinical and research purposes. However, most standard methods are built for proprietary measures from specific devices that are not widely used. In this study, we develop an algorithm for classifying sleep and awake using a single day of triaxial accelerometer data, which can be acquired from all smart devices. This algorithm consists of two stages, clustering and hidden Markov modeling, and outperforms standard algorithms in sensitivity (94%), specificity (93 %), and overall accuracy (93%) across seven subjects. This method can help automate actigraphy analyses at scale using widely available technology using even a single day's worth of data. Clinical Relevance- Automated monitoring of patients' activity at home can help track recovery trajectories after surgery and injury, disease progression, treatment response.

Measuring sleep parameters of naval sailors: A comparison between subjective self-report and wrist actigraphy

Wrist actigraph and self-report activity logs were used in a Royal Canadian Navy's at-sea exercise to track sleep patterns of naval personnel. In this study, we compared sleep parameters obtained from two measurement methods and investigated their intrinsic biases. The results revealed a strong agreement between two methods for recording sleep offset times, but a relatively poor agreement for parameters that include substantial periods of transition between sleep and wake states. Overall, self-reported sleep durations were substantially longer than actigraphic estimates (mean bias of -30.6 min; limits of agreement -95.9 to 34.8 min), and the discrepancy was mainly caused by differences in two methods to track sleep onset latency and Wake-up After Sleep Onset (WASO). Based on a customised activity log, a strong positive correlation (rho = 0.75, p < .001) between self-report and actigraphy was observed for sleep duration estimates, which confirmed the effectiveness of the activity log in field studies. Between two participant groups with different work schedules, the agreement between self-report and actigraphy was consistently better for day workers than watch keepers. The findings inform future sleep research planning that involves naval personnel in field settings.

Age Trends in Actigraphy and Self-Report Sleep Across the Life Span: Findings From the Pittsburgh Lifespan Sleep Databank

OBJECTIVE

Sleep changes over the human life span, and it does so across multiple dimensions. We used individual-level cross-sectional data to characterize age trends and sex differences in actigraphy and self-report sleep dimensions across the healthy human life span.

METHODS

The Pittsburgh Lifespan Sleep Databank consists of harmonized participant-level data from sleep-related studies conducted at the University of Pittsburgh (2003-2019). We included data from 1065 (n = 577 female; 21 studies) Pittsburgh Lifespan Sleep Databank participants aged 10 to 87 years without a major psychiatric, sleep, or medical condition. All participants completed wrist actigraphy and the self-rated Pittsburgh Sleep Quality Index. Main outcomes included actigraphy and self-report sleep duration, efficiency, and onset/offset timing, and actigraphy variability in midsleep timing.

RESULTS

We used generalized additive models to examine potentially nonlinear relationships between age and sleep characteristics and to examine sex differences. Actigraphy and self-report sleep onset time shifted later between ages 10 and 18 years (23:03-24:10 [actigraphy]; 21:58-23:53 [self-report]) and then earlier during the 20s (00:08-23:40 [actigraphy]; 23:50-23:34 [self-report]). Actigraphy and self-report wake-up time also shifted earlier during the mid-20s through late 30s (07:48-06:52 [actigraphy]; 07:40-06:41 [self-report]). Self-report, but not actigraphy, sleep duration declined between ages 10 and 20 years (09:09-07:35). Self-report sleep efficiency decreased over the entire life span (96.12-93.28), as did actigraphy variability (01:54-01:31).

CONCLUSIONS

Awareness of age trends in multiple sleep dimensions in healthy individuals-and explicating the timing and nature of sex differences in age-related change-can suggest periods of sleep-related risk or resilience and guide intervention efforts.

Assessing Walking Programs in Fibromyalgia: A Concordance Study between Measures

This study analyzes the degree of agreement between three self-report measures (Walking Behavior, WALK questionnaire and logbooks) assessing adherence to walking programs through reporting their components (minutes, rests, times a week, consecutive weeks) and their concordance with a standard self-report of physical activity (IPAQ-S questionnaire) and an objective, namely number of steps (pedometer), in 275 women with fibromyalgia. Regularized partial correlation networks were selected as the analytic framework. Three network models based on two different times of assessment, namely T1 and T2, including 6 weeks between both, were used. WALK and the logbook were connected with Walking Behavior and also with the IPAQ-S. The logbook was associated with the pedometers (Z-score > 1 in absolute value). When the behavior was assessed specifically and in a detailed manner, participants’ results for the different self-report measures were in agreement. Specific self-report methods provide detailed information that is consistent with validated self-report measures (IPAQ-S) and objective measures (pedometers). The self-report measures that assess the behavioral components of physical activity are useful when studying the implementation of walking as physical exercise.

Rest-Activity Rhythm Is Associated With Obesity Phenotypes: A Cross-Sectional Analysis

BACKGROUND

The prevalence of obesity continues to increase in spite of substantial efforts towards its prevention, posing a major threat to health globally. Circadian disruption has been associated with a wide range of preclinical and clinical disorders, including obesity. However, whether rest-activity rhythm (RAR), an expression of the endogenous circadian rhythm, is associated with excess adiposity is poorly understood. Here we aimed to assess the association of RAR with general and abdominal obesity.

METHODS

Non-institutionalized adults aged ≥20 years participating in the US National Health and Nutrition Examination Survey (NHANES) 2011-2014 who wore accelerometers for at least four 24-hour periods were included (N=7,838). Amplitude, mesor, acrophase and pseudo-F statistic of RAR were estimated using extended cosinor model, and interdaily stability (IS) and intradaily variability (IV) were computed by nonparametric methods. We tested the association between rest-activity rhythm and general obesity defined by body mass index and abdominal obesity by waist circumference. Waist-to-height ratio, sagittal abdominal diameter, and total and trunk fat percentages measured by imaging methods were also analyzed.

RESULTS

In multivariable analysis, low amplitude (magnitude of the rhythm), mesor (rhythm-corrected average activity level), pseudo-F statistic (robustness of the rhythm), IS (day-to-day rhythm stability), or high IV (rhythm fragmentation) were independently associated with higher likelihood of general or abdominal obesity (all Ps<.05). Consistently, RAR metrics were similarly associated with all adiposity measures (all Ps<.01). Delayed phase of RAR (later acrophase) was only significantly related to general and abdominal obesity in women.

CONCLUSIONS

Aberrant RAR is independently associated with anthropometric and imaging measures of general and abdominal obesity. Longitudinal studies assessing whether RAR metrics can predict weight gain and incident obesity are warranted.

Deep-Learning Approach to Predict Survival Outcomes Using Wearable Actigraphy Device Among End-Stage Cancer Patients

Survival prediction is highly valued in end-of-life care clinical practice, and patient performance status evaluation stands as a predominant component in survival prognostication. While current performance status evaluation tools are limited to their subjective nature, the advent of wearable technology enables continual recordings of patients' activity and has the potential to measure performance status objectively. We hypothesize that wristband actigraphy monitoring devices can predict in-hospital death of end-stage cancer patients during the time of their hospital admissions. The objective of this study was to train and validate a long short-term memory (LSTM) deep-learning prediction model based on activity data of wearable actigraphy devices. The study recruited 60 end-stage cancer patients in a hospice care unit, with 28 deaths and 32 discharged in stable condition at the end of their hospital stay. The standard Karnofsky Performance Status score had an overall prognostic accuracy of 0.83. The LSTM prediction model based on patients' continual actigraphy monitoring had an overall prognostic accuracy of 0.83. Furthermore, the model performance improved with longer input data length up to 48 h. In conclusion, our research suggests the potential feasibility of wristband actigraphy to predict end-of-life admission outcomes in palliative care for end-stage cancer patients. Clinical Trial Registration: The study protocol was registered on ClinicalTrials.gov (ID: NCT04883879).