Monitoring Activity and Gait in Children (MAGIC) using digital health technologies.

BACKGROUND: Digital health technologies (DHTs) can collect gait and physical activity in adults, but limited studies have validated these in children. This study compared gait and physical activity metrics collected using DHTs to those collected by reference comparators during in-clinic sessions, to collect a normative accelerometry dataset, and to evaluate participants' comfort and their compliance in wearing the DHTs at-home. METHODS: The MAGIC (Monitoring Activity and Gait in Children) study was an analytical validation study which enrolled 40, generally healthy participants aged 3-17 years. Gait and physical activity were collected using DHTs in a clinical setting and continuously at-home. RESULTS: Overall good to excellent agreement was observed between gait metrics extracted with a gait algorithm from a lumbar-worn DHT compared to ground truth reference systems. Majority of participants either "agreed" or "strongly agreed" that wrist and lumbar DHTs were comfortable to wear at home, respectively, with 86% (wrist-worn DHT) and 68% (lumbar-worn DHT) wear-time compliance. Significant differences across age groups were observed in multiple gait and activity metrics obtained at home. CONCLUSIONS: Our findings suggest that gait and physical activity data can be collected from DHTs in pediatric populations with high reliability and wear compliance, in-clinic and in home environments. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04823650 IMPACT: Digital health technologies (DHTs) have been used to collect gait and physical activity in adult populations, but limited studies have validated these metrics in children. The MAGIC study comprehensively validates the performance and feasibility of DHT-measured gait and physical activity in the pediatric population. Our findings suggest that reliable gait and physical activity data can be collected from DHTs in pediatric populations, with both high accuracy and wear compliance both in-clinic and in home environments. The identified across-age-group differences in gait and activity measurements highlighted their potential clinical value.

Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?

Wearable accelerometers allow for continuous monitoring of function and behaviors in the participant's naturalistic environment. Devices are typically worn in different body locations depending on the concept of interest and endpoint under investigation. The lumbar and wrist are commonly used locations: devices placed at the lumbar region enable the derivation of spatio-temporal characteristics of gait, while wrist-worn devices provide measurements of overall physical activity (PA). Deploying multiple devices in clinical trial settings leads to higher patient burden negatively impacting compliance and data quality and increases the operational complexity of the trial. In this work, we evaluated the joint information shared by features derived from the lumbar and wrist devices to assess whether gait characteristics can be adequately represented by PA measured with wrist-worn devices. Data collected at the Pfizer Innovation Research (PfIRe) Lab were used as a real data example, which had around 7 days of continuous at-home data from wrist- and lumbar-worn devices (GENEActiv) obtained from a group of healthy participants. The relationship between wrist- and lumbar-derived features was estimated using multiple statistical methods, including penalized regression, principal component regression, partial least square regression, and joint and individual variation explained (JIVE). By considering multilevel models, both between- and within-subject effects were taken into account. This work demonstrated that selected gait features, which are typically measured with lumbar-worn devices, can be represented by PA features measured with wrist-worn devices, which provides preliminary evidence to reduce the number of devices needed in clinical trials and to increase patients' comfort. Moreover, the statistical methods used in this work provided an analytic framework to compare repeated measures collected from multiple data modalities.

Wrist-Worn Accelerometry, Aging, and Gait Speed in the Baltimore Longitudinal Study of Aging.

Wrist-worn accelerometry metrics are not well defined in older adults. Accelerometry data from 720 participants (mean age 70 years, 55% women) were summarized into (a) total activity counts per day, (b) active minutes per day, (c) active bouts per day, and (d) activity fragmentation (the reciprocal of the mean active bout length). Linear regression and mixed-effects models were utilized to estimate associations between age and gait speed with wrist accelerometry. Activity counts per day, daily active minutes per day, and active bouts per day were negatively associated with age among all participants, while positive associations with activity fragmentation were only observed among those ≥65 years. More activity counts, more daily active minutes, and lower activity fragmentation were associated with faster gait speed. There were baseline age interactions with annual changes in total activity counts per day, active minutes per day, and activity fragmentation (Baseline age × Time, p < .01 for all). These results help define and characterize changes in wrist-based physical activity patterns among older adults.

Identifying neural signatures mediating behavioral symptoms and psychosis onset: High-dimensional whole brain functional mediation analysis.

Along the pathway from behavioral symptoms to the development of psychotic disorders sits the multivariate mediating brain. The functional organization and structural topography of large-scale multivariate neural mediators among patients with brain disorders, however, are not well understood. Here, we design a high-dimensional brain-wide functional mediation framework to investigate brain regions that intermediate between baseline behavioral symptoms and future conversion to full psychosis among individuals at clinical high risk (CHR). Using resting-state functional magnetic resonance imaging (fMRI) data from 263 CHR subjects, we extract an α brain atlas and a ß brain atlas: the former underlines brain areas associated with prodromal symptoms and the latter highlights brain areas associated with disease onset. In parallel, we identify and separate mediators that potentially positively and negatively mediate symptoms and psychosis, respectively, and quantify the effect of each neural mediator on disease development. Taken together, these results paint a brain-wide picture of neural markers that are potentially mediating behavioral symptoms and the development of psychotic disorders; additionally, they underscore a statistical framework that is useful to uncover large-scale intermediating variables in a regulatory biological system.

Demographic characteristics associated with circadian rest-activity rhythm patterns: a cross-sectional study.

BACKGROUND: Rest-activity rhythm (RAR), a manifestation of circadian rhythms, has been associated with morbidity and mortality risk. However, RAR patterns in the general population and specifically the role of demographic characteristics in RAR pattern have not been comprehensively assessed. Therefore, we aimed to describe RAR patterns among non-institutionalized US adults and age, sex, and race/ethnicity variation using accelerometry data from a nationally representative population. METHODS: This cross-sectional study was conducted using the US National Health and Nutrition Examination Survey (NHANES) 2011-2014. Participants aged ≥20 years who were enrolled in the physical activity monitoring examination and had at least four 24-h periods of valid wrist accelerometer data were included in the present analysis. 24-h RAR metrics were generated using both extended cosinor model (amplitude, mesor, acrophase and pseudo-F statistic) and nonparametric methods (interdaily stability [IS] and intradaily variability [IV]). Multivariable linear regression was used to assess the association between RAR and age, sex, and race/ethnicity. RESULTS: Eight thousand two hundred participants (mean [SE] age, 49.1 [0.5] years) were included, of whom 52.2% were women and 67.3% Whites. Women had higher RAR amplitude and mesor, and also more robust (pseudo-F statistic), more stable (higher IS) and less fragmented (lower IV) RAR (all P trend < 0.001) than men. Compared with younger adults (20-39 years), older adults (≥ 60 years) exhibited reduced RAR amplitude and mesor, but more stable and less fragmented RAR, and also reached their peak activity earlier (advanced acrophase) (all P trend < 0.001). Relative to other racial/ethnic groups, Hispanics had the highest amplitude and mesor level, and most stable (highest IS) and least fragmented (lowest IV) RAR pattern (P trend < 0.001). Conversely, non-Hispanic blacks had the lowest peak activity level (lowest amplitude) and least stable (lowest IS) RAR pattern (all P trend < 0.001). CONCLUSIONS: In the general adult population, RAR patterns vary significantly according to sex, age and race/ethnicity. These results may reflect demographic-dependent differences in intrinsic circadian rhythms and may have important implications for understanding racial, ethnic, sex and other disparities in morbidity and mortality risk.

Novel metrics for growth model selection.

BACKGROUND: Literature surrounding the statistical modeling of childhood growth data involves a diverse set of potential models from which investigators can choose. However, the lack of a comprehensive framework for comparing non-nested models leads to difficulty in assessing model performance. This paper proposes a framework for comparing non-nested growth models using novel metrics of predictive accuracy based on modifications of the mean squared error criteria. METHODS: Three metrics were created: normalized, age-adjusted, and weighted mean squared error (MSE). Predictive performance metrics were used to compare linear mixed effects models and functional regression models. Prediction accuracy was assessed by partitioning the observed data into training and test datasets. This partitioning was constructed to assess prediction accuracy for backward (i.e., early growth), forward (i.e., late growth), in-range, and on new-individuals. Analyses were done with height measurements from 215 Peruvian children with data spanning from near birth to 2 years of age. RESULTS: Functional models outperformed linear mixed effects models in all scenarios tested. In particular, prediction errors for functional concurrent regression (FCR) and functional principal component analysis models were approximately 6% lower when compared to linear mixed effects models. When we weighted subject-specific MSEs according to subject-specific growth rates during infancy, we found that FCR was the best performer in all scenarios. CONCLUSION: With this novel approach, we can quantitatively compare non-nested models and weight subgroups of interest to select the best performing growth model for a particular application or problem at hand.

Re-evaluating the effect of age on physical activity over the lifespan.

Advancements in accelerometer analytic and visualization techniques allow researchers to more precisely identify and compare critical periods of physical activity (PA) decline by age across the lifespan, and describe how daily PA patterns may vary across age groups. We used accelerometer data from the 2003-2006 cohorts of the National Health and Nutrition Examination Survey (NHANES) (n=12,529) to quantify total PA as well as PA by intensity across the lifespan using sex-stratified, age specific percentile curves constructed using generalized additive models. We additionally estimated minute-to-minute diurnal PA using smoothed bivariate surfaces. We found that from childhood to adolescence (ages 6-19) across sex, PA is sharply lower by age partially due to a later initiation of morning PA. Total PA levels, at age 19 are comparable to levels at age 60. Contrary to prior evidence, during young adulthood (ages 20-30) total and light intensity PA increases by age and then stabilizes during midlife (ages 31-59) partially due to an earlier initiation of morning PA. We additionally found that males compared to females have an earlier lowering in PA by age at midlife and lower total PA, higher sedentary behavior, and lower light intensity PA in older adulthood; these trends seem to be driven by lower PA in the afternoon compared to females. Our results suggest a re-evaluation of how emerging adulthood may affect PA levels and the importance of considering time of day and sex differences when developing PA interventions.

Integrative Modeling of Accelerometry-Derived Sleep, Physical Activity, and Circadian Rhythm Domains With Current or Remitted Major Depression.

Importance: Accelerometry has been increasingly used as an objective index of sleep, physical activity, and circadian rhythms in people with mood disorders. However, most prior research has focused on sleep or physical activity alone without consideration of the strong within- and cross-domain intercorrelations; and few studies have distinguished between trait and state profiles of accelerometry domains in major depressive disorder (MDD). Objectives: To identify joint and individual components of the domains derived from accelerometry, including sleep, physical activity, and circadian rhythmicity using the Joint and Individual Variation Explained method (JIVE), a novel multimodal integrative dimension-reduction technique; and to examine associations between joint and individual components with current and remitted MDD. Design, Setting, and Participants: This cross-sectional study examined data from the second wave of a population cohort study from Lausanne, Switzerland. Participants included 2317 adults (1164 without MDD, 185 with current MDD, and 968 with remitted MDD) with accelerometry for at least 7 days. Statistical analysis was conducted from January 2021 to June 2023. Main Outcomes and Measures: Features derived from accelerometry for 14 days; current and remitted MDD. Logistic regression adjusted for age, sex, body mass index, and anxiety and substance use disorders. Results: Among 2317 adults included in the study, 1261 (54.42%) were female, and mean (SD) age was 61.79 (9.97) years. JIVE reduced 28 accelerometry features to 3 joint and 6 individual components (1 sleep, 2 physical activity, 3 circadian rhythms). Joint components explained 58.5%, 79.5%, 54.5% of the total variation in sleep, physical activity, and circadian rhythm domains, respectively. Both current and remitted depression were associated with the first 2 joint components that were distinguished by the salience of high-intensity physical activity and amplitude of circadian rhythm and timing of both sleep and physical activity, respectively. MDD had significantly weaker circadian rhythmicity. Conclusions and Relevance: Application of a novel multimodal dimension-reduction technique demonstrates the importance of joint influences of physical activity, circadian rhythms, and timing of both sleep and physical activity with MDD; dampened circadian rhythmicity may constitute a trait marker for MDD. This work illustrates the value of accelerometry as a potential biomarker for subtypes of depression and highlights the importance of consideration of the full 24-hour sleep-wake cycle in future studies.

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

STUDY OBJECTIVES: To compare sleep and 24-hour rest/activity rhythms (RARs) between cognitively normal older adults who are ß-amyloid-positive (Aß+) or Aß- 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 ±â€…8.5 years, 55% female, 76% white) with wrist actigraphy data and Aß+ versus Aß- 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 Aß+ and 57 Aß- participants. RESULTS: Compared to Aß- participants, Aß+ 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, Aß+ 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 Aß-related differences in standard sleep or RAR metrics. CONCLUSIONS: Findings suggest Aß+ older adults have higher, more stable day-to-day afternoon/evening activity than Aß- 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 Aß deposition and if they predict clinical dementia and agitation in the afternoon/evening (i.e. "sundowning").

SciKit digital health package for accelerometry-measured physical activity: comparisons to existing solutions and investigations of age effects in healthy adults.

Introduction: Accelerometry has become increasingly prevalent to monitor physical activity due to its low participant burden, quantitative metrics, and ease of deployment. Physical activity metrics are ideal for extracting intuitive, continuous measures of participants' health from multiple days or weeks of high frequency data due to their fairly straightforward computation. Previously, we released an open-source digital health python processing package, SciKit Digital Health (SKDH), with the goal of providing a unifying device-agnostic framework for multiple digital health algorithms, such as activity, gait, and sleep. Methods: In this paper, we present the open-source SKDH implementation for the derivation of activity endpoints from accelerometer data. In this implementation, we include some non-typical features that have shown promise in providing additional context to activity patterns, and provide comparisons to existing algorithms, namely GGIR and the GENEActiv macros. Following this reference comparison, we investigate the association between age and derived physical activity metrics in a healthy adult cohort collected in the Pfizer Innovation Research Lab, comprising 7-14 days of at-home data collected from younger (18-40 years) and older (65-85 years) healthy volunteers. Results: Results showed that activity metrics derived with SKDH had moderate to excellent ICC values (0.550 to 1.0 compared to GGIR, 0.469 to 0.697 compared to the GENEActiv macros), with high correlations for almost all compared metrics (>0.733 except vs GGIR sedentary time, 0.547). Several features show age-group differences, with Cohen's d effect sizes >1.0 and p-values < 0.001. These features included non-threshold methods such as intensity gradient, and activity fragmentation features such as between-states transition probabilities. Discussion: These results demonstrate the validity of the implemented SKDH physical activity algorithm, and the potential of the implemented PA metrics in assessing activity changes in free-living conditions.

Decentralized clinical trials and rare diseases: a Drug Information Association Innovative Design Scientific Working Group (DIA-IDSWG) perspective.

BACKGROUND: Traditional clinical trials require tests and procedures that are administered in centralized clinical research sites, which are beyond the standard of care that patients receive for their rare and chronic diseases. The limited number of rare disease patients scattered around the world makes it particularly challenging to recruit participants and conduct these traditional clinical trials. MAIN BODY: Participating in clinical research can be burdensome, especially for children, the elderly, physically and cognitively impaired individuals who require transportation and caregiver assistance, or patients who live in remote locations or cannot afford transportation. In recent years, there is an increasing need to consider Decentralized Clinical Trials (DCT) as a participant-centric approach that uses new technologies and innovative procedures for interaction with participants in the comfort of their home. CONCLUSION: This paper discusses the planning and conduct of DCTs, which can increase the quality of trials with a specific focus on rare diseases.

The roles, challenges, and merits of the p value.

Since the 18th century, the p value has been an important part of hypothesis-based scientific investigation. As statistical and data science engines accelerate, questions emerge: to what extent are scientific discoveries based on p values reliable and reproducible? Should one adjust the significance level or find alternatives for the p value? Inspired by these questions and everlasting attempts to address them, here, we provide a systematic examination of the p value from its roles and merits to its misuses and misinterpretations. For the latter, we summarize modest recommendations to handle them. In parallel, we present the Bayesian alternatives for seeking evidence and discuss the pooling of p values from multiple studies and datasets. Overall, we argue that the p value and hypothesis testing form a useful probabilistic decision-making mechanism, facilitating causal inference, feature selection, and predictive modeling, but that the interpretation of the p value must be contextual, considering the scientific question, experimental design, and statistical principles.

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.

Considerations to address missing data when deriving clinical trial endpoints from digital health technologies.

Digital health technologies (DHTs) enable us to measure human physiology and behavior remotely, objectively and continuously. With the accelerated adoption of DHTs in clinical trials, there is an unmet need to identify statistical approaches to address missing data to ensure that the derived endpoints are valid, accurate, and reliable. It is not obvious how commonly used statistical methods to handle missing data in clinical trials can be directly applied to the complex data collected by DHTs. Meanwhile, current approaches used to address missing data from DHTs are of limited sophistication and focus on the exclusion of data where the quantity of missing data exceeds a given threshold. High-frequency time series data collected by DHTs are often summarized to derive epoch-level data, which are then processed to compute daily summary measures. In this article, we discuss characteristics of missing data collected by DHT, review emerging statistical approaches for addressing missingness in epoch-level data including within-patient imputations across common time periods, functional data analysis, and deep learning methods, as well as imputation approaches and robust modeling appropriate for handling missing data in daily summary measures. We discuss strategies for minimizing missing data by optimizing DHT deployment and by including the patients' perspectives in the study design. We believe that these approaches provide more insight into preventing missing data when deriving digital endpoints. We hope this article can serve as a starting point for further discussion among clinical trial stakeholders.

Comparing three wearable accelerometers to measure early activity after cardiac surgery.

Objective: Wearable activity monitors can provide detailed data on activity after cardiac surgery and discriminate a patient's risk for hospital-based outcomes. However, comparative data for different monitoring approaches, as well as predictive ability over clinical characteristics, are lacking. In addition, data on specific thresholds of activity are needed. The objective of this study was to compare 3 wearable activity monitors and 1 observational mobility scale in discriminating risk for 3 hospital-based outcomes, and to establish clinically relevant step thresholds. Methods: Cardiac surgery patients were enrolled between June 2016 and August 2017 in a cohort study. Postoperative activity was measured by 3 accelerometry monitors (StepWatch Ambulation Monitor, Fitbit Charge HR, and ActiGraph GT9X) and 1 nurse-based observation scale. Monitors represent a spectrum of characteristics, including wear location (ankle/wrist), output (activity counts/steps), consumer accessibility, and cost. Primary outcomes were duration of hospitalization >7 days, discharge to a nonhome location, and 30-day readmission. Results: Data were available from 193 patients (median age 67 years [interquartile range, 58-72]). All postoperative day 2 activity metrics (ie, from StepWatch, Fitbit, ActiGraph, and the observation scale) were independently associated with prolonged hospitalization and discharge to a nonhome location. Only steps as measured by StepWatch was independently associated with 30-day readmission. Overall, StepWatch provided the greatest discrimination (C-statistics 0.71-0.76 for all outcomes). Step thresholds between 250 and 500 steps/day identified between 74% and 96% of patients with any primary outcome. Conclusions: Data from wearable accelerometers provide additive value in early postoperative risk-stratification for hospital-based outcomes. These results both support and provide guidance for activity-monitoring programs after cardiac surgery.

Analysis of Hearing Loss and Physical Activity Among US Adults Aged 60-69 Years.

Importance: Hearing loss may be a modifiable factor associated with decreased physical activity in older adults. Objective: To examine the association of hearing loss with objectively measured physical activity, including moderate-to-vigorous physical activity, light-intensity physical activity, sedentary behavior, and pattern of physical activity (physical activity fragmentation). Design, Setting, and Participants: This population-based cross-sectional study used National Health and Nutrition Examination Survey (NHANES) data collected in the 2003 to 2004 cycle and analyzed in 2017 to 2020. Participants aged 60 to 69 years with complete audiometry, physical activity, and comorbidity data were included in the analysis. Data analysis was performed from January 2017 to December 2020. Exposures: Hearing defined by the pure tone average (PTA; range, 0.5-4 kHz) in the better ear, with normal PTA defined as less than 25 dB hearing loss, mild hearing loss defined as PTA 25 to less than 40 dB hearing loss, and moderate or greater hearing loss defined as a PTA greater than or equal to 40 dB hearing loss. Main Outcomes and Measures: The primary outcomes were comprehensive metrics of objectively measured physical activity, including time spent in moderate-to-vigorous physical activity, light-intensity physical activity, and sedentary behavior, and physical activity fragmentation. Linear regression was used to model the association between hearing loss and physical activity. Results: Of the 291 participants (mean [SD] age, 64.53 [2.96] years), 139 (47.8%) were male, 48 (16.5%) had mild hearing loss, and 22 (7.6%) had moderate or greater hearing loss. After adjusting for age, sex, education, race/ethnicity, and comorbidities, hearing loss (vs normal hearing) was significantly associated with less time spent in moderate-to-vigorous physical activity by 5.53 minutes per day (95% CI, -10.15 to -0.90 minutes per day), less time spent in light-intensity physical activity by 28.55 minutes per day (95% CI, -53.07 to -4.02 minutes per day), more time spent in sedentary behaviors by 34.07 minutes per day (95% CI, 8.32 to 59.82 minutes per day), and more fragmented physical activity pattern by 0.38 SD higher in active-to-sedentary transition probability (95% CI, to 0.10 to 0.65). The magnitude of the association of hearing loss (vs normal hearing) with physical activity metrics was equivalent to 7.28 years (95% CI, 3.19 to 11.37 years) of accelerated age for moderate-to-vigorous physical activity, 5.84 years (95% CI, 1.45 to 10.23 years) of accelerated age for light-intensity physical activity, and 10.53 years (95% CI, 2.89 to 18.16 years) of accelerated age for degree of physical activity fragmentation. Conclusions and Relevance: These findings suggest that hearing loss is associated with a worse physical activity profile. Whether interventions to address hearing loss in adults could improve physical activity profiles will require further study.

Brain amyloid burden, sleep, and 24-hour rest/activity rhythms: screening findings from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's and Longitudinal Evaluation of Amyloid Risk and Neurodegeneration Studies.

STUDY OBJECTIVES: To examine in a subsample at the screening phase of a clinical trial of a ß-amyloid (Aß) antibody whether disturbed sleep and altered 24-hour rest/activity rhythms (RARs) may serve as markers of preclinical Alzheimer's disease (AD). METHODS: Overall, 26 Aß-positive (Aß+) and 33 Aß-negative (Aß-) cognitively unimpaired participants (mean age = 71.3 ± 4.6 years, 59% women) from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) and the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) studies, respectively, wore actigraphs for 5.66 ± 0.88 24-hour periods. We computed standard sleep parameters, standard RAR metrics (mean estimating statistic of rhythm, amplitude, acrophase, interdaily stability, intradaily variability, relative amplitude), and performed a novel RAR analysis (function-on-scalar regression [FOSR]). RESULTS: We were unable to detect any differences between Aß+ and Aß- participants in standard sleep parameters or RAR metrics with our sample size. When we used novel FOSR methods, however, Aß+ participants had lower activity levels than Aß- participants in the late night through early morning (11:30 pm to 3:00 am), and higher levels in the early morning (4:30 am to 8:30 am) and from midday through late afternoon (12:30 pm to 5:30 pm; all p < .05). Aß+ participants also had higher variability in activity across days from 9:30 pm to 1:00 am and 4:30 am to 8:30 am, and lower variability from 2:30 am to 3:30 am (all p < .05). CONCLUSIONS: Although we found no association of preclinical AD with standard actigraphic sleep or RAR metrics, a novel data-driven analytic method identified temporally "local" RAR alterations in preclinical AD.

Development of digital measures for nighttime scratch and sleep using wrist-worn wearable devices.

Patients with atopic dermatitis experience increased nocturnal pruritus which leads to scratching and sleep disturbances that significantly contribute to poor quality of life. Objective measurements of nighttime scratching and sleep quantity can help assess the efficacy of an intervention. Wearable sensors can provide novel, objective measures of nighttime scratching and sleep; however, many current approaches were not designed for passive, unsupervised monitoring during daily life. In this work, we present the development and analytical validation of a method that sequentially processes epochs of sample-level accelerometer data from a wrist-worn device to provide continuous digital measures of nighttime scratching and sleep quantity. This approach uses heuristic and machine learning algorithms in a hierarchical paradigm by first determining when the patient intends to sleep, then detecting sleep-wake states along with scratching episodes, and lastly deriving objective measures of both sleep and scratch. Leveraging reference data collected in a sleep laboratory (NCT ID: NCT03490877), results show that sensor-derived measures of total sleep opportunity (TSO; time when patient intends to sleep) and total sleep time (TST) correlate well with reference polysomnography data (TSO: r = 0.72, p < 0.001; TST: r = 0.76, p < 0.001; N = 32). Log transformed sensor derived measures of total scratching duration achieve strong agreement with reference annotated video recordings (r = 0.82, p < 0.001; N = 25). These results support the use of wearable sensors for objective, continuous measurement of nighttime scratching and sleep during daily life.

Perfusion Pressure Lacks Diagnostic Specificity for the Diagnosis of Acute Compartment Syndrome.

OBJECTIVE: To evaluate the diagnostic performance of perfusion pressure (PP) thresholds for fasciotomy. DESIGN: Prospective observational study. SETTING: Seven Level-1 trauma centers. PATIENTS/PARTICIPANTS: One hundred fifty adults with severe leg injuries and ≥2 hours of continuous PP data who had been enrolled in a multicenter observational trial designed to develop a clinical prediction rule for acute compartment syndrome (ACS). MAIN OUTCOME MEASUREMENTS: For each patient, a given PP criterion was positive if it was below the specified threshold for at least 2 consecutive hours. The diagnostic performance of PP thresholds between 10 and 30 mm Hg was determined using 2 reference standards for comparison: (1) the likelihood of ACS as determined by an expert panel who reviewed each patient's data portfolio or (2) whether the patient underwent fasciotomy. RESULTS: Using the likelihood of ACS as the diagnostic standard (ACS considered present if median likelihood ≥70%, absent if <30%), a PP threshold of 30 mm Hg had diagnostic sensitivity 0.83, specificity 0.53, positive predictive value 0.07, and negative predictive value 0.99. Results were insensitive to more strict likelihood categorizations and were similar for other PP thresholds between 10- and 25-mm Hg. Using fasciotomy as the reference standard, the same PP threshold had diagnostic sensitivity 0.50, specificity 0.50, positive predictive value 0.04, negative predictive value 0.96. CONCLUSION: No value of PP from 10 to 30 mm Hg had acceptable diagnostic performance, regardless of which reference diagnostic standard was used. These data question current practice of diagnosing ACS based on PP and suggest the need for further research. LEVEL OF EVIDENCE: Diagnostic Level I. See Instructions for Authors for a complete description of levels of evidence.