Identifying Person-Specific Drivers of Depression in Adolescents: Protocol for a Smartphone-Based Ecological Momentary Assessment and Passive Sensing Study.

BACKGROUND: Adolescence is marked by an increasing risk of depression and is an optimal window for prevention and early intervention. Personalizing interventions may be one way to maximize therapeutic benefit, especially given the marked heterogeneity in depressive presentations. However, empirical evidence that can guide personalized intervention for youth is lacking. Identifying person-specific symptom drivers during adolescence could improve outcomes by accounting for both developmental and individual differences. OBJECTIVE: This study leverages adolescents' everyday smartphone use to investigate person-specific drivers of depression and validate smartphone-based mobile sensing data against established ambulatory methods. We describe the methods of this study and provide an update on its status. After data collection is completed, we will address three specific aims: (1) identify idiographic drivers of dynamic variability in depressive symptoms, (2) test the validity of mobile sensing against ecological momentary assessment (EMA) and actigraphy for identifying these drivers, and (3) explore adolescent baseline characteristics as predictors of these drivers. METHODS: A total of 50 adolescents with elevated symptoms of depression will participate in 28 days of (1) smartphone-based EMA assessing depressive symptoms, processes, affect, and sleep; (2) mobile sensing of mobility, physical activity, sleep, natural language use in typed interpersonal communication, screen-on time, and call frequency and duration using the Effortless Assessment of Risk States smartphone app; and (3) wrist actigraphy of physical activity and sleep. Adolescents and caregivers will complete developmental and clinical measures at baseline, as well as user feedback interviews at follow-up. Idiographic, within-subject networks of EMA symptoms will be modeled to identify each adolescent's person-specific drivers of depression. Correlations among EMA, mobile sensor, and actigraph measures of sleep, physical, and social activity will be used to assess the validity of mobile sensing for identifying person-specific drivers. Data-driven analyses of mobile sensor variables predicting core depressive symptoms (self-reported mood and anhedonia) will also be used to assess the validity of mobile sensing for identifying drivers. Finally, between-subject baseline characteristics will be explored as predictors of person-specific drivers. RESULTS: As of October 2023, 84 families were screened as eligible, of whom 70% (n=59) provided informed consent and 46% (n=39) met all inclusion criteria after completing baseline assessment. Of the 39 included families, 85% (n=33) completed the 28-day smartphone and actigraph data collection period and follow-up study visit. CONCLUSIONS: This study leverages depressed adolescents' everyday smartphone use to identify person-specific drivers of adolescent depression and to assess the validity of mobile sensing for identifying these drivers. The findings are expected to offer novel insights into the structure and dynamics of depressive symptomatology during a sensitive period of development and to inform future development of a scalable, low-burden smartphone-based tool that can guide personalized treatment decisions for depressed adolescents. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/43931.

Rest-Activity Rhythm Differences in Acute Rehabilitation Between Poststroke Patients and Non-Brain Disease Controls: Comparative Study.

BACKGROUND: Circadian rhythm disruptions are a common concern for poststroke patients undergoing rehabilitation and might negatively impact their functional outcomes. OBJECTIVE: Our research aimed to uncover unique patterns and disruptions specific to poststroke rehabilitation patients and identify potential differences in specific rest-activity rhythm indicators when compared to inpatient controls with non-brain-related lesions, such as patients with spinal cord injuries. METHODS: We obtained a 7-day recording with a wearable actigraphy device from 25 poststroke patients (n=9, 36% women; median age 56, IQR 46-71) and 25 age- and gender-matched inpatient control participants (n=15, 60% women; median age 57, IQR 46.5-68.5). To assess circadian rhythm, we used a nonparametric method to calculate key rest-activity rhythm indicators-relative amplitude, interdaily stability, and intradaily variability. Relative amplitude, quantifying rest-activity rhythm amplitude while considering daily variations and unbalanced amplitudes, was calculated as the ratio of the difference between the most active 10 continuous hours and the least active 5 continuous hours to the sum of these 10 and 5 continuous hours. We also examined the clinical correlations between rest-activity rhythm indicators and delirium screening tools, such as the 4 A's Test and the Barthel Index, which assess delirium and activities of daily living. RESULTS: Patients who had a stroke had higher least active 5-hour values compared to the control group (median 4.29, IQR 2.88-6.49 vs median 1.84, IQR 0.67-4.34; P=.008). The most active 10-hour values showed no significant differences between the groups (stroke group: median 38.92, IQR 14.60-40.87; control group: median 31.18, IQR 18.02-46.84; P=.93). The stroke group presented a lower relative amplitude compared to the control group (median 0.74, IQR 0.57-0.85 vs median 0.88, IQR 0.71-0.96; P=.009). Further analysis revealed no significant differences in other rest-activity rhythm metrics between the two groups. Among the patients who had a stroke, a negative correlation was observed between the 4 A's Test scores and relative amplitude (ρ=-0.41; P=.045). Across all participants, positive correlations emerged between the Barthel Index scores and both interdaily stability (ρ=0.34; P=.02) and the most active 10-hour value (ρ=0.42; P=.002). CONCLUSIONS: This study highlights the relevance of circadian rhythm disruptions in poststroke rehabilitation and provides insights into potential diagnostic and prognostic implications for rest-activity rhythm indicators as digital biomarkers.

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 (ß=-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.

Device-estimated sleep metrics do not mediate the relation between race and blood pressure dipping in young black and white women.

Short, disturbed, and irregular sleep may contribute to blunted nocturnal blood pressure (BP) dipping, a predictor of cardiovascular disease. Black women (BLW) demonstrate less BP dipping and poorer sleep health than White women (WHW). However, it remains unclear whether device-estimated sleep health metrics mediate the relation between race and BP dipping in young women. We hypothesized that the relation between race and BP dipping would be partly mediated by sleep health metrics of sleep duration, sleep efficiency, and sleep regularity. Participants (20 BLW, 17 WHW) were 18-29 years old, normotensive, nonobese, and without evidence of sleep disorders. Systolic and diastolic BP dipping were derived from 24-h ambulatory BP monitoring. Habitual sleep duration and sleep efficiency were estimated via 14 days of wrist actigraphy. Sleep duration regularity was calculated as the standard deviation (SD) of nightly sleep duration (SDSD). Sleep timing regularity metrics were calculated as the SD of sleep onset and sleep midpoint (SMSD). Mediation analysis tested the mediating effect of each sleep metric on the relation between race and BP dipping. BLW experienced less systolic (P = .02) and diastolic (P = .01) BP dipping. Sleep duration (P = .14) was not different between groups. BLW had lower sleep efficiency (P < .01) and higher SDSD (P = .02), sleep onset SD (P < .01) and SMSD (P = .01). No sleep metrics mediated the relation between race and BP dipping (all indirect effects P > .38). In conclusion, mediation pathways of sleep health metrics do not explain racial differences in nocturnal BP dipping between young BLW and WHW.

Actigraph-based quantification of sleep in children with dystonia undergoing deep brain stimulation.

OBJECTIVE: Dystonia is among the most common pediatric movement disorders and can manifest with a range of debilitating symptoms, including sleep disruptions. The duration and quality of sleep are strongly associated with quality of life in these individuals and could serve as biomarkers of dystonia severity and the efficacy of interventions such as deep brain stimulation (DBS). Thus, this study investigated sleep duration and its relationship to disease severity and DBS response in pediatric dystonia. METHODS: Actigraphs (wearable three-axis accelerometers) were used to record multiday sleep data in 22 children with dystonia, including 6 patients before and after DBS implantation, and age- and sex- matched healthy controls. Data were preprocessed, and metrics of sleep duration and quality were extracted. Repeated-measures statistical analyses were used. RESULTS: Children with dystonia slept less than typically developing children (p = 0.009), and shorter sleep duration showed trending correlation with worse dystonia severity (r = -0.421, p = 0.073). Of 4 patients who underwent DBS and had good-quality data, 1 demonstrated significantly improved sleep (p < 0.001) postoperatively. Reduction in dystonia severity strongly correlated with increased sleep duration after DBS implantation (r = -0.965, p = 0.035). CONCLUSIONS: Sleep disturbances are an underrecognized marker of pediatric dystonia severity, as well as the effectiveness of interventions such as DBS. They can serve as objective biomarkers of disease burden and symptom progression after treatment.

Effectiveness of pedometer-based walking programmes in improving some modifiable risk factors of stroke among community-dwelling older adults: a systematic review, theoretical synthesis and meta-analysis.

BACKGROUND: Pedometer-based walking programs hold promise as a health promotion strategy for stroke prevention in community-dwelling older adults, particularly when targeted at physical activity-related modifiable risk factors. The question arises: What is the effectiveness of pedometer-based walking program interventions in improving modifiable stroke risk factors among community-dwelling older adults? METHOD: Eight databases were searched up to December 2nd, 2023, following the Preferred Reporting Items for Systematic Review and Meta-Analysis protocol. Inclusion criteria focused on randomized controlled trials (RCTS) involving community-dwelling older adults and reported in English. Two independent reviewers utilized Physiotherapy Evidence Database (PEDro) tool to extract data, assess eligibility, evaluate study quality, and identify potential bias. Standardized mean difference (SMD) was employed as summary statistics for primary -physical activity level -and secondary outcomes related to cardiovascular function (blood pressure) and metabolic syndrome, including obesity (measured by body mass index and waist circumference), fasting blood sugar, glycated hemoglobin, high-density lipoprotein cholesterol (HDL-C), and triglycerides. A random-effects model was used to generate summary estimates of effects. RESULTS: The review analyzed eight studies involving 1546 participants aged 60-85 years, with 1348 successfully completing the studies. Across these studies, pedometer-based walking programs were implemented 2-3 times per week, with sessions lasting 40-60 minutes, over a duration of 4-26 weeks. The risk of bias varied from high to moderate. Our narrative synthesis revealed positive trends in HDL-C levels, fasting blood sugar, and glycated hemoglobin, suggesting improved glycemic control and long-term blood sugar management. However, the impact on triglycerides was only marginal. Primary meta-analysis demonstrated significantly improved physical activity behavior (SMD=0.44,95%CI:0.26, 0.61,p=<0.00001;I2=0%;4 studies; 532 participants) and systolic blood pressure (SMD=-0.34,95%CI:-0.59,-0.09;p=<0.008;I2=65%,2 studies;249 participants), unlike diastolic blood pressure (SMD=0.13,95%CI:-0.13,-0.38,p=0.33; I2=91%; 2 studies; 237 participants). Interventions based on social cognitive, self-efficacy, and self-efficiency theory(ies), and social cognitive theory applied in an ecological framework, were linked to successful physical activity behavior outcomes. CONCLUSION: Pedometer-based walking programs, utilizing interpersonal health behavior theory/ecological framework, enhance physical activity behavior and have antihypertensive effects in community-dwelling older adults. While they do not significantly affect diastolic blood pressure, these programs potentially serve as a primary stroke prevention strategy aligning with global health goals. TRIAL REGISTRATION: Registration Number: INPLASY202230118.

Quantifying Nocturnal Scratch in Atopic Dermatitis: A Machine Learning Approach Using Digital Wrist Actigraphy.

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.

Activity-Based Prospective Memory in Insomniacs.

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.

Comparing Human-Smartphone Interactions and Actigraphy Measurements for Circadian Rhythm Stability and Adiposity: Algorithm Development and Validation Study.

BACKGROUND: This study aimed to investigate the relationships between adiposity and circadian rhythm and compare the measurement of circadian rhythm using both actigraphy and a smartphone app that tracks human-smartphone interactions. OBJECTIVE: We hypothesized that the app-based measurement may provide more comprehensive information, including light-sensitive melatonin secretion and social rhythm, and have stronger correlations with adiposity indicators. METHODS: We enrolled a total of 78 participants (mean age 41.5, SD 9.9 years; 46/78, 59% women) from both an obesity outpatient clinic and a workplace health promotion program. All participants (n=29 with obesity, n=16 overweight, and n=33 controls) were required to wear a wrist actigraphy device and install the Rhythm app for a minimum of 4 weeks, contributing to a total of 2182 person-days of data collection. The Rhythm app estimates sleep and circadian rhythm indicators by tracking human-smartphone interactions, which correspond to actigraphy. We examined the correlations between adiposity indices and sleep and circadian rhythm indicators, including sleep time, chronotype, and regularity of circadian rhythm, while controlling for physical activity level, age, and gender. RESULTS: Sleep onset and wake time measurements did not differ significantly between the app and actigraphy; however, wake after sleep onset was longer (13.5, SD 19.5 minutes) with the app, resulting in a longer actigraphy-measured total sleep time (TST) of 20.2 (SD 66.7) minutes. The obesity group had a significantly longer TST with both methods. App-measured circadian rhythm indicators were significantly lower than their actigraphy-measured counterparts. The obesity group had significantly lower interdaily stability (IS) than the control group with both methods. The multivariable-adjusted model revealed a negative correlation between BMI and app-measured IS (P=.007). Body fat percentage (BF%) and visceral adipose tissue area (VAT) showed significant correlations with both app-measured IS and actigraphy-measured IS. The app-measured midpoint of sleep showed a positive correlation with both BF% and VAT. Actigraphy-measured TST exhibited a positive correlation with BMI, VAT, and BF%, while no significant correlation was found between app-measured TST and either BMI, VAT, or BF%. CONCLUSIONS: Our findings suggest that IS is strongly correlated with various adiposity indicators. Further exploration of the role of circadian rhythm, particularly measured through human-smartphone interactions, in obesity prevention could be warranted.

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.

Use of actigraphy for monitoring agitation and rest-activity cycles in patients with acute traumatic brain injury in the ICU.

BACKGROUND: In traumatic brain injury patients (TBI) admitted to the intensive care unit (ICU), agitation can lead to accidental removal of catheters, devices as well as self-extubation and falls. Actigraphy could be a potential tool to continuously monitor agitation. The objectives of this study were to assess the feasibility of monitoring agitation with actigraphs and to compare activity levels in agitated and non-agitated critically ill TBI patients. METHODS: Actigraphs were placed on patients' wrists; 24-hour monitoring was continued until ICU discharge or limitation of therapeutic efforts. Feasibility was assessed by actigraphy recording duration and missing activity count per day. RESULTS: Data from 25 patients were analyzed. The mean number of completed day of actigraphy per patient was 6.5 ± 5.1. The mean missing activity count was 20.3 minutes (±81.7) per day. The mean level of activity measured by raw actigraphy counts per minute over 24 hours was higher in participants with agitation than without agitation. CONCLUSIONS: This study supports the feasibility of actigraphy use in TBI patients in the ICU. In the acute phase of TBI, agitated patients have higher levels of activity, confirming the potential of actigraphy to monitor agitation.

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 ß ^ : 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.

The National Sleep Research Resource: making data findable, accessible, interoperable, reusable and promoting sleep science.

This paper presents a comprehensive overview of the National Sleep Research Resource (NSRR), a National Heart Lung and Blood Institute-supported repository developed to share data from clinical studies focused on the evaluation of sleep disorders. The NSRR addresses challenges presented by the heterogeneity of sleep-related data, leveraging innovative strategies to optimize the quality and accessibility of available datasets. It provides authorized users with secure centralized access to a large quantity of sleep-related data including polysomnography, actigraphy, demographics, patient-reported outcomes, and other data. In developing the NSRR, we have implemented data processing protocols that ensure de-identification and compliance with FAIR (Findable, Accessible, Interoperable, Reusable) principles. Heterogeneity stemming from intrinsic variation in the collection, annotation, definition, and interpretation of data has proven to be one of the primary obstacles to efficient sharing of datasets. Approaches employed by the NSRR to address this heterogeneity include (1) development of standardized sleep terminologies utilizing a compositional coding scheme, (2) specification of comprehensive metadata, (3) harmonization of commonly used variables, and (3) computational tools developed to standardize signal processing. We have also leveraged external resources to engineer a domain-specific approach to data harmonization. We describe the scope of data within the NSRR, its role in promoting sleep and circadian research through data sharing, and harmonization of large datasets and analytical tools. Finally, we identify opportunities for approaches for the field of sleep medicine to further support data standardization and sharing.

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).

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).

Low-dose exogenous melatonin plus evening dim light and time in bed scheduling advances circadian phase irrespective of measured or estimated dim light melatonin onset time: preliminary findings.

STUDY OBJECTIVES: The purpose of the present study was to preliminarily evaluate whether knowing the dim light melatonin onset (DLMO) time is advantageous when treating delayed sleep-wake phase disorder with low-dose melatonin treatment plus behavioral interventions (ie, evening dim light and time in bed scheduling). METHODS: In this randomized, controlled, double-blind trial, 40 adults with delayed sleep-wake phase disorder were randomly assigned to 4 weeks of 0.5 mg timed to be administered either 3 hours before the DLMO (measured DLMO group, n = 20) or 5 hours before sleep-onset time per actigraphy (estimated DLMO group, n = 20), in conjunction with behavioral interventions. The primary outcome was change in the DLMO (measured in-home). Secondary outcomes included sleep parameters per diary and actigraphy (sleep-onset and -offset times and total sleep time), Morningness-Eveningness Questionnaire, Multidimensional Fatigue Inventory, PROMIS-Sleep Disturbance, PROMIS-Sleep Related Impairment, and Pittsburgh Sleep Quality Index. Mixed-effects models tested for group differences in these outcome. RESULTS: After applying the Bonferroni correction for multiple comparisons (significant P value set at < .004), there were significant main effects for visit on all outcomes except for the Pittsburgh Sleep Quality Index and total sleep time per wrist actigraphy and diary. There were no group-by-visit interactions for any of the outcomes (P > .004). CONCLUSIONS: Scheduled low-dose melatonin plus behavioral interventions may improve many circadian and sleep parameters regardless of whether melatonin administration is scheduled based on estimated or measured DLMO. A larger-scale trial is needed to confirm these preliminary findings. CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Name: The Clinical Utility of Measuring the Circadian Clock in Treatment of Delayed Sleep-Wake Phase Disorder; URL: https://clinicaltrials.gov/study/NCT03715465; Identifier: NCT03715465. CITATION: Swanson LM, de Sibour T, DuBuc K, et al. Low-dose exogenous melatonin plus evening dim light and time in bed scheduling advances circadian phase irrespective of measured or estimated dim light melatonin onset time: preliminary findings. J Clin Sleep Med. 2024;20(7):1131-1140.

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.

Validation of a sleep staging classification model for healthy adults based on two combinations of a single-channel EEG headband and wrist actigraphy.

STUDY OBJECTIVES: The aim of this study was to develop a sleep staging classification model capable of accurately performing on different wearable devices. METHODS: Twenty-three healthy participants underwent a full-night type I polysomnography and used two device combinations: (A) flexible single-channel electroencephalogram (EEG) headband + actigraphy (n = 12) and (B) rigid single-channel EEG headband + actigraphy (n = 11). The signals were segmented into 30-second epochs according to polysomnographic stages (scored by a board-certified sleep technologist; model ground truth) and 18 frequency and time features were extracted. The model consisted of an ensemble of bagged decision trees. Bagging refers to bootstrap aggregation to reduce overfitting and improve generalization. To evaluate the model, a training dataset under 5-fold cross-validation and an 80-20% dataset split was used. The headbands were also evaluated without the actigraphy feature. Participants also completed a usability evaluation (comfort, pain while sleeping, and sleep disturbance). RESULTS: Combination A had an F1-score of 98.4% and the flexible headband alone of 97.7% (error rate for N1: combination A = 9.8%; flexible headband alone = 15.7%). Combination B had an F1-score of 96.9% and the rigid headband alone of 95.3% (error rate for N1: combination B = 17.0%; rigid headband alone = 27.7%); in both, N1 was more confounded with N2. CONCLUSIONS: We developed an accurate sleep classification model based on a single-channel EEG device, and actigraphy was not an important feature of the model. Both headbands were found to be useful, with the rigid one being more disruptive to sleep. Future research can improve our results by applying the developed model in a population with sleep disorders. CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Name: Actigraphy, Wearable EEG Band and Smartphone for Sleep Staging; URL: https://clinicaltrials.gov/study/NCT04943562; Identifier: NCT04943562. CITATION: Melo MC, Vallim JRS, Garbuio S, et al. Validation of a sleep staging classification model for healthy adults based on 2 combinations of a single-channel EEG headband and wrist actigraphy. J Clin Sleep Med. 2024;20(6):983-990.

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.