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The aims of this study were to investigate the associations of major depressive disorder (MDD) and its subtypes (atypical, melancholic, combined, unspecified) with actigraphy-derived measures of sleep, physical activity and circadian rhythms; and test the potentially mediating role of sleep, physical activity and circadian rhythms in the well-established associations of the atypical MDD subtype with Body Mass Index (BMI) and the metabolic syndrome (MeS). The sample consisted of 2317 participants recruited from an urban area, who underwent comprehensive somatic and psychiatric evaluations. MDD and its subtypes were assessed via semi-structured diagnostic interviews. Sleep, physical activity and circadian rhythms were measured using actigraphy. MDD and its subtypes were associated with several actigraphy-derived variables, including later sleep midpoint, low physical activity, low inter-daily stability and larger intra-individual variability of sleep duration and relative amplitude. Sleep midpoint and physical activity fulfilled criteria for partial mediation of the association between atypical MDD and BMI, and physical activity also for partial mediation of the association between atypical MDD and MeS. Our findings confirm associations of MDD and its atypical subtype with sleep and physical activity, which are likely to partially mediate the associations of atypical MDD with BMI and MeS, although most of these associations are not explained by sleep and activity variables. This highlights the need to consider atypical MDD, sleep and sedentary behavior as cardiovascular risk factors.

The Mobile Motor Activity Research Consortium for Health (mMARCH) is a collaborative network of clinical and community studies that employ common digital mobile protocols and collect common clinical and biological measures across participating studies. At a high level, a key scientific goal which spans mMARCH studies is to develop a better understanding of the interrelationships between physical activity (PA), sleep (SL), and circadian rhythmicity (CR) and mental and physical health in children, adolescents, and adults. mMARCH studies employ wrist-worn accelerometry to obtain objective measures of PA/SL/CR. However, there is currently no consensus on a standard data processing pipeline for raw accelerometry data and few open-source tools which facilitate their development. The R package GGIR is the most prominent open-source software package for processing raw accelerometry data, offering great functionality and substantial user flexibility. However, even with GGIR, processing done in a harmonized and reproducible fashion across multiple analytical centers requires a nontrivial amount of expertise combined with a careful implementation. In addition, there are many statistical methods useful for analyzing PA/SL/CR patterns using accelerometry data which are implemented in non-GGIR R packages, including methods from multivariate statistics, functional data analysis, distributional data analysis, and time series analyses. To address the issues of multisite harmonization and additional feature creation, mMARCH developed a streamlined harmonized and reproducible pipeline for loading and cleaning raw accelerometry data via GGIR, merging GGIR, and non-GGIR features of PA/SL/CR together, implementing several additional data and feature quality checks, and performing multiple analyses including Joint and Individual Variation Explained, an unsupervised machine learning dimension reduction technique that identifies latent factors capturing joint across and individual to each of three domains of PA/SL/CR. The pipeline is easily modified to calculate additional features of interest, and allows for studies not affiliated with mMARCH to apply a pipeline which facilitates direct comparisons of scientific results in published work by mMARCH studies. This manuscript describes the pipeline and illustrates the use of combined GGIR and non-GGIR features by applying Joint and Individual Variation Explained to the accelerometry component of CoLaus|PsyCoLaus, one of mMARCH sites. The pipeline is publicly available via open-source R package mMARCH.AC.

Background This study examined associations of actigraphy-estimated sleep parameters with concurrent and future cognitive performance in adults aged ≥ 50 years and explored interactions with race. Methods Participants were 435 cognitively normal adults in the Baltimore Longitudinal Study of Aging who completed wrist actigraphy at baseline (mean = 6.6 nights) and underwent longitudinal testing of memory, attention, executive function, language, and visuospatial ability. On average, participants with follow-up data were followed for 3.1 years. Primary predictors were baseline mean total sleep time, sleep onset latency, sleep efficiency (SE), and wake after sleep onset (WASO). Fully adjusted linear mixed-effects models included demographics, baseline health-related characteristics, smoking status, sleep medication use, APOE e4 carrier status, and interactions of each covariate with time. Results In adjusted models, higher SE (per 10%; B = 0.11, p = .012) and lower WASO (per 30 minutes; B = −0.12, p = .007) were associated with better memory cross-sectionally. In contrast, higher SE was associated with greater visuospatial ability decline longitudinally (B = −0.02, p = .004). Greater WASO was associated with poorer visuospatial ability cross-sectionally (B = −0.09, p = .019) but slower declines in visuospatial abilities longitudinally (B = 0.02, p = .002). Several sleep-cognition cross-sectional and longitudinal associations were stronger in, or limited to, Black participants (compared to White participants). Conclusions This study suggests cross-sectional sleep-cognition associations differ across distinct objective sleep parameters and cognitive domains. This study also provides preliminary evidence for racial differences across some sleep-cognition relationships. Unexpected directions of associations between baseline sleep and cognitive performance over time may be attributable to the significant proportion of participants without follow-up data and require further investigation.

Sleep and physical activity, two important health behaviors, are often studied independently using different accelerometer types and body locations. Understanding whether accelerometers designed for monitoring each behavior can provide similar sleep parameter estimates may help determine whether one device can be used to measure both behaviors. 331 adults (70.7±13.7 years) from the Baltimore Longitudinal Study of Aging (BLSA) wore the ActiGraph GT9X Link and the Actiwatch 2 simultaneously on the non-dominant wrist for 7.0±1.6 nights. Total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, number of wake bouts, mean wake bout length, and sleep fragmentation index (SFI) were extracted from ActiGraph using the Cole-Kripke algorithm and from Actiwatch using the software default algorithm. These parameters were compared using paired t-tests, Bland-Altman plots, and Deming regression models. Stratified analyses were performed by age, sex, and BMI. Compared to the Actiwatch, the ActiGraph estimated comparable TST and sleep efficiency, but fewer wake bouts, longer WASO, longer wake bout length, and higher SFI (all p

Objective: To examine whether and how sex and age modify the association between accelerometer-based physical activity (PA) and metabolic syndrome (MetS) among American (US) adults. Method: Adults aged ≥20 years old who participated in the mobile center examination during 2003–2006 in the National Health and Nutrition Examination Survey were included for analysis. The total minutes per day of moderate-to-vigorous PA (MVPA) was estimated using ActiGraph. Multivariable logistic regression was used to estimate the odds ratio (OR) of having MetS at an increasing MVPA time. The modification effects of gender and age on the association between MetS and MVPA time were examined by testing for two-way and three-way interaction terms of MVPA time, sex, and age in the model after adjusting for relevant covariates. Results: The prevalence of MetS generally decreased with the MVPA time and was lower in females than in males, although the sex difference varied across age groups. After adjusting for demographic and lifestyle covariates, there was a significant sex difference in how an increased MVPA time lowered the odds of MetS. This interactive effect also varied with age. MVPA benefitted young and middle-age populations up until about 65 years old for both sexes, and the protective effect weakened with age. Although the effect of MVPA was stronger for males than females at young ages, the rate at which it attenuated was quicker in males. The OR of MetS between males and females per unit change of MVPA time was 0.73 (95% CI: [0.57, 0.93]) at age = 25 years, compared to OR = 1.00 (95% CI: [0.88, 1.16]) at age = 60 years. Before the age of 50, the gender difference in the protective effect on MetS was larger at low MVPA levels and became smaller at higher MVPA levels. The male advantage was quite stable with an increasing MVPA time for ages 50–60, and no longer significant at older ages. Conclusions: Young and middle-age populations benefitted from MVPA, lowering the risk of MetS for both sexes. A longer MVPA time was associated with a greater decrease in the risk of MetS in young men than in women, but the sex difference reduced with age and was no longer apparent in older populations.

Background Human motor function is optimised for energetic efficiency, however, age-related neurodegenerative changes affects neuromotor control of walking. Energy utilisation has been associated with motor performance, but its association with cognitive performance is unknown. Methods The study population included 979 Baltimore Longitudinal Study of Aging participants aged $\ge$50 years (52% female, mean age: 70$\pm$10.2 years) with a median follow-up time of 4.7 years. Energy utilisation for walking was operationalised as a ratio of the energy cost of slow walking to peak walking energy expenditure during standardised tasks (‘cost-ratio’). Cognitive functioning was measured using the Trail Making Tests, California Verbal Learning Test, Wechsler Adult Intelligence Scale (WAIS), letter and category fluency and card rotation tests. Linear mixed models adjusted for demographics, education and co-morbidities assessed the association between baseline cost-ratio and cognitive functioning, cross-sectionally and longitudinally. To investigate the relationship among those with less efficient energy utilisation, subgroup analyses were performed. Results In fully adjusted models, a higher cost-ratio was cross-sectionally associated with poorer performance on all cognitive tests except WAIS (P Conclusions These findings suggest cross-sectional and longitudinal links between energy utilisation and cognitive performance, highlighting an intriguing link between brain function and the energy needed for ambulation. Future research should examine this association earlier in the life course to gauge the potential for interventive mechanisms.

BACKGROUND Interest in quitting smoking is common among young adults who smoke, but can prove challenging. Although evidence-based smoking cessation interventions exist and are effective, a lack of access to these interventions specifically designed for young adults remains a major barrier for this population to successfully quit smoking. Therefore, researchers have begun to develop modern, smartphone-based interventions to deliver smoking cessation messages at the appropriate place and time for an individual. A promising approach is the delivery of interventions using geofences - spatial buffers around high-risk locations for smoking that trigger intervention messages when an individual’s phone enters the perimeter. Despite a growth in providing personalized and ubiquitous smoking cessation interventions, few studies have incorporated spatial methods to optimize intervention delivery using place and time information. OBJECTIVE This study demonstrates an exploratory method of generating person-specific geofences around high-risk areas for smoking by presenting four case studies, combining self-reported smartphone-based surveys and passively tracked location data. The study also examines which geofence construction method could inform a subsequent study design that will automate the process to deploy coping messages when young adults enter geofence boundaries. METHODS Data came from an Ecological Momentary Assessment (EMA) study with young adult smokers conducted from 2016 to 2017 in the San Francisco Bay Area. Participants reported smoking and non-smoking events through a smartphone app for 30 days and GPS data we recorded by the app. We sampled four cases along EMA compliance quartiles and constructed person-specific geofences around locations with self-reported smoking events for each three-hour time interval using zones with normalized mean kernel density estimates exceeding 0.7. We assessed the percentage of smoking events captured within geofences constructed for three types of zones (Census blocks; 500 ft.2 and 1,000 ft.2 fishnet grids). Descriptive comparisons were made across the four cases to better understand the strengths and limitations of each geofence construction method. RESULTS The number of reported past 30-day smoking events ranged from 12 to 177 for the four cases. Each three-hour geofences for three of the four cases captured over 50% of smoking events. The 1,000 ft2 fishnet grid captured the highest percentage of smoking events compared to Census blocks across the four cases. Across three-hour periods except for 3 – 5:59 am for one case, geofences contained an average of 36.4% – 100.0% of smoking events. Findings showed that fishnet grid geofences may capture more smoking events compared to Census blocks. CONCLUSIONS Findings suggest that this geofence construction method can identify high-risk smoking situations by time and place and has potential for generating individually-tailored geofences for smoking cessation intervention delivery. In a subsequent smartphone-based smoking cessation intervention study, we plan to use fishnet grid geofences to inform delivery of intervention messages. CLINICALTRIAL

Background Physical activity becomes increasingly fragmented with age, which may be an early marker of functional decline. Energetic cost of walking and energy capacity are also linked with functional decline, but their associations with activity fragmentation, and the potential modifying roles of total daily physical activity and age, remains unclear. Method A total of 493 participants (50–93 years) from the Baltimore Longitudinal Study of Aging underwent measures of energetic cost of usual-paced overground walking (mL/kg/m), energy demand during slow walking (mL/kg/min) on a treadmill (0.67 m/s, 0% grade), and average peak walking energy expenditure (mL/kg/min) during a fast-paced 400-m walk. A ratio of slow walking to peak walking energy expenditure (“cost-to-capacity ratio”) was calculated. Activity fragmentation was quantified as an active-to-sedentary transition probability (ASTP) using Actiheart accelerometer data. Linear regression models with ASTP as the dependent variable were used to test whether poorer energy cost and capacity were associated with higher ASTP and whether the associations differed by daily physical activity or age. Results After adjusting for demographics, body composition, comorbidities, and daily physical activity, every 10% higher cost-to-capacity ratio was associated with 0.4% greater ASTP (p = .005). This association was primarily driven by the least active participants (pinteraction = .023). Peak walking energy expenditure was only associated with ASTP among participants aged ≥70 years. Conclusions Higher cost-to-capacity ratio and lower energy capacity may manifest as more fragmented physical activity, especially among those less active or aged ≥70 years. Future studies should examine whether an increasing cost-to-capacity ratio or declining energy capacity predicts subsequent activity fragmentation.

Background: The increasing popularity of wrist-worn accelerometers introduces novel challenges to the research on physical activity and sedentary behavior. Estimation of body posture is one such challenge. Methods: The authors proposed an approach called SedUp to differentiate between sedentary (sitting/lying) and standing postures. SedUp is based on the logistic regression classifier, using the wrist elevation and the motion variability extracted from raw accelerometry data collected on the axis parallel to the forearm. The authors developed and tested our method on data from N = 45 community-dwelling older adults. All subjects wore ActiGraph GT3X+ accelerometers on the left and right wrist, and activPAL was placed on the thigh in the free-living environment for 7 days. ActivPAL provided ground truth about body posture. The authors reported SedUp’s classification accuracy for each wrist separately. Results: Using the data from the left wrist, SedUp estimated the standing posture with median true positive rate = 0.83 and median true negative rate = 0.91. Using the data from the right wrist, SedUp estimated the standing posture with median true positive rate = 0.86 and median true negative rate = 0.93. Conclusions: SedUp provides accurate classification of body posture using wrist-worn accelerometers. The separate validation for each wrist allows for the application of SedUp in a wide spectrum of free-living studies.

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

Despite a rapid expansion of cannabis use for pain management, how cannabis and prescription opioids are co-used and whether co-use improves analgesia and promotes reduction of opioid use in the daily lives of individuals with chronic pain is poorly understood. Based upon ecological momentary assessment (EMA), the present study examined 1) how pain and use of opioids and/or cannabis in the previous moment is associated with individuals' choice of opioids and/or cannabis in the next moment, 2) the effects of co-use on pain severity and pain relief, and 3) whether daily total opioid consumption differs on days when people only used opioids versus co-used. Adults with chronic pain (N = 46) using both opioids and cannabis who were recruited online completed a 30-day EMA. Elevated pain did not increase the likelihood of co-use in subsequent momentary assessments. Switching from sole use of either opioids and cannabis to co-use was common. Neither co-use nor sole use of either cannabis or opioids were associated with reductions in pain in the next moment. However, participants reported the highest daily perceived pain relief from co-use compared to cannabis and opioid use only. Post hoc analysis suggested recall bias as a potential source of this discrepant findings between momentary versus retrospective assessment. Lastly, there was no evidence of an opioid-sparing effect of cannabis in this sample. The present study shows preliminary evidence on cannabis and opioid co-use patterns, as well as the effects of co-use on pain and opoid dose in the real-world setting. PERSPECTIVE: This article presents the overall patterns and effects of co-using cannabis and prescription opioids among individuals with chronic pain employing ecological momentary assessment. There were conflicting findings on the association between co-use and analgesia. Co-use was not associated with a reduction in daily opioid consumption in this sample.

Background Larger brain volumes are often associated with more free-living physical activity (PA) in cognitively normal older adults. Yet, whether greater brain volumes are associated with more favorable (less fragmented) PA patterns, and whether this association is stronger than with total PA, remains unknown. Methods Brain magnetic resonance imaging and wrist-worn accelerometer data were collected in 301 participants (mean age = 77 [SD = 7] years, 59% women) enrolled in the Baltimore Longitudinal Study of Aging. Linear regression models were fit to examine whether brain volumes (cc) were cross-sectionally associated with: (a) total daily PA minutes and (b) activity fragmentation (mean number of PA bouts / total PA minutes × 100). Sensitivity analyses were conducted by adjusting for counterpart PA variables (eg, fragmentation covariate included in the PA minutes model). Results Greater white matter volumes in the parietal and temporal lobes were associated with higher daily PA minutes (2.6 [SE = 1.0] and 3.8 [0.9] min/day, respectively; p < .009 for both) after adjusting for demographics, behavioral factors, medical conditions, gait speed, apolipoprotein E e4 status, and intracranial volume. Greater temporal white matter volume was associated with lower fragmentation (−0.16% [0.05], p = .003). In sensitivity analyses, observed associations between brain volumes and daily PA minutes remained significant while associations with fragmentation no longer remained significant. Conclusions Our results suggest white matter brain structure in cognitively normal older adults is associated with the total amount of PA and, to a lesser extent, the PA accumulation patterns. More work is needed to elucidate the longitudinal relationship between brain structure and function and PA patterns with aging.

Biometric Monitoring Technologies (BioMeTs) are becoming increasingly common to aid data collection in clinical trials and practice. The state of BioMeTs, and associated digitally measured biomarkers, is highly reminiscent of the field of laboratory biomarkers two decades ago. In this review, we have summarized and leveraged historical perspectives, and lessons learned from laboratory biomarkers as they apply to BioMeTs. Both categories share common features, including goals and roles in biomedical research, definitions, and many elements of the biomarker qualification framework. They can also be classified based on the underlying technology, each with distinct features and performance characteristics, which require bench and human experimentation testing phases. In contrast to laboratory biomarkers, digitally measured biomarkers require prospective data collection for purposes of analytical validation in human subjects, lack well-established and widely accepted performance characteristics, require human factor testing and, for many applications, access to raw (sample-level) data. Novel methods to handle large volumes of data, as well as security and data rights requirements add to the complexity of this emerging field. Our review highlights the need for a common framework with appropriate vocabulary and standardized approaches to evaluate digitally measured biomarkers, including defining performance characteristics and acceptance criteria. Additionally, the need for human factor testing drives early patient engagement during technology development. Finally, the use of BioMeTs requires a relatively high degree of technology literacy among both study participants and healthcare professionals. Transparency of data generation and the need for novel analytical and statistical tools creates opportunities for precompetitive collaborations.

The use of digital health products has gained considerable interest as a new way to improve therapeutic research and development. Although these products are being adopted by various industries and stakeholders, their incorporation in clinical trials has been slow due to a disconnect between the promises of digital products and potential risks in using these new technologies in the absence of regulatory support. The Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium hosted a public workshop to address challenges and opportunities in this field. Important characteristics of tool development were addressed in a series of presentations, case studies, and open panel sessions. The workshop participants endorsed the usefulness of an evidentiary criteria framework, highlighted the importance of early patient engagement, and emphasized the potential impact of digital monitoring tools and precompetitive collaborations. Concerns were expressed about the lack of real‐life validation examples and the limitations of legacy standards used as a benchmark for novel tool development and validation. Participants recognized the need for novel analytical and statistical approaches to accommodate analyses of these novel data types. Future directions are to harmonize definitions to build common methodologies and foster multidisciplinary collaborations; to develop approaches toward integrating digital monitoring data with the totality of the data in clinical trials, and to continue an open dialog in the community. There was a consensus that all these efforts combined may create a paradigm shift of how clinical trials are planned, conducted, and results brought to regulatory reviews.

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

Shape restrictions on functional regression coefficients such as non-negativity, monotonicity, convexity or concavity are often available in the form of a prior knowledge or required to maintain a structural consistency in functional regression models. A new estimation method is developed in shape-constrained functional regression models using Bernstein polynomials. Specifically, estimation approaches from nonparametric regression are extended to functional data, properly accounting for shape-constraints in a large class of functional regression models such as scalar-on-function regression (SOFR), function-on-scalar regression (FOSR), and function-on-function regression (FOFR). Theoretical results establish the asymptotic consistency of the constrained estimators under standard regularity conditions. A projection based approach provides point-wise asymptotic confidence intervals for the constrained estimators. A bootstrap test is developed facilitating testing of the shape constraints. Numerical analysis using simulations illustrate improvement in efficiency of the estimators from the use of the proposed method under shape constraints. Two applications include i) modeling a drug effect in a mental health study via shape-restricted FOSR and ii) modeling subject-specific quantile functions of accelerometry-estimated physical activity in the Baltimore Longitudinal Study of Aging (BLSA) as outcomes via shape-restricted quantile-function on scalar regression (QFOSR). R software implementation and illustration of the proposed estimation method and the test is provided.

Introduction Neuropsychiatric symptoms (NPS) affect nearly all individuals with AD. Agitation in AD (Agit-AD) is one of the most common NPS, with an estimated prevalence over 40%. Agit-AD is a major cause of distress and burden for AD caregivers and may be driven by disrupted circadian rhythms and sleep. In healthy individuals, the circadian clock that generates these rhythms regulates patterns in mood, wakefulness, and other physiologic processes, such as body temperature and hormone levels. Individuals with Agit-AD may experience disturbance in their circadian rhythms such that aggressive and disruptive behavior is increased. This project aims to examine if agitated AD subjects (AgitAD+) express delayed circadian phases and reduced rhythm strength as compared to non-agitated AD subjects (AgitAD-) and healthy controls. Additionally, we seek to 1) evaluate if circadian phase and rhythm strength vary between subtypes of Agit-AD (affective dysregulation vs. executive dysfunction); and to 2) compare sleep amount, sleep quality, and degree of sleep disordered breathing among AgitAD+ subjects vs. AgitAD- subjects and healthy controls. Methods We studied 25 subjects (Table 1): 15 AgitAD+ subjects, 8 AgitAD- subjects, and 2 age-matched healthy controls (HC). Participants completed wrist actigraphy for one week to quantify rest/activity rhythms and sleep; consumed an ingestible, disposable temperature sensor for 1-2 days to quantify core body temperature, a marker of the endogenous circadian rhythm; and completed a single night of home sleep testing (HST; Table 2). Inclusion criteria were diagnosis of AD and age 55-90 years, and exclusion criteria include seizures, significant tremors, delirium, using CPAP for sleep apnea, restless legs syndrome, and circadian rhythm disorders. NPS were measured with the Neuropsychiatric Inventory-Clinician Version (NPI-C), the Cohen Mansfield Agitation Inventory (CMAI), the Neurobehavioral Rating Scale (NBRS), and the Cornell Scale for Depression in Dementia (CSDD). Agit-AD was defined using criteria from the International Psychogeriatric Association. AgitAD+ subjects had clinically significant agitation, defined as NPI-C Agitation or Aggression subtotals ≥ 4. Disease progression was measured using the Clinical Dementia Rating Scale (CDR), and subjects underwent a physical and neurological exam. Cognition was assessed using the Montreal Cognitive Assessment (MOCA), the Hopkins Verbal Learning Test (HVLT), the Controlled Oral Word Association Test (COWAT), Animal Naming (AN), and the Trail-Making Test (parts A & B). Sleep questionnaires included the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), and the Morningness-Eveningness Questionnaire (MEQ). Caregivers were asked to complete a sleep diary for subjects and an agitation log over the week they completed actigraphy. Due to the COVID-19 pandemic, we implemented hybrid visits with telemedicine to limit in-person interactions. Results 25 subjects have completed the study to date. There were no statistically significant differences observed in actigraphy-derived sleep parameters between agitated and non-agitated AD subjects (2-group t-test); see Table 1). However, significant differences between the groups have been observed in the rest/activity rhythms: compared to subjects without agitation (AgitAD-), AgitAD+ subjects had lower levels of activity in the morning and greater activity in the afternoon and evening (Figure 1). We also have performed function-on-scalar regression (FOSR) (Goldsmith et al., 2015, Biometrics 71:344-353) using subject-specific activity profiles as outcomes and age and agitation/control status as predictors. Figure 2 shows functional effects of age on rest/activity rhythm (left panel) and the functional effect of agitation status (right panel); the red background shows times of day during which there were statistically significant differences, using the pointwise method of analysis (p Conclusions The collection of multimodal noninvasive data on sleep, activity, and core body temperature is feasible in agitated AD outpatients. This study could provide evidence for CRD/sleep disturbance as a driver of Agit-AD and could identify Agit-AD subtypes who are most likely to benefit from chronobiotic or sleep interventions for Agit-AD. Furthermore, this study could provide evidence for the use of actigraphy as a surrogate biomarker in proof-of-concept intervention studies targeting Agit-AD. Funding National Institute on Aging, R01AG054771

We introduce a novel method of principal component analysis for data with varying domain lengths for each functional observation. We refer to this technique as variable-domain functional principal ...

Accurate and reliable quantification of brain metabolites measured in vivo using 1H magnetic resonance spectroscopy (MRS) is a topic of continued interest. Aside from differences in the basic approach to quantification, the quantification of metabolite data acquired at different sites and on different platforms poses an additional methodological challenge. In this study, spectrally edited γ-aminobutyric acid (GABA) MRS data were analyzed and GABA levels were quantified relative to an internal tissue water reference. Data from 284 volunteers scanned across 25 research sites were collected using GABA+ (GABA + co-edited macromolecules (MM)) and MM-suppressed GABA editing. The unsuppressed water signal from the volume of interest was acquired for concentration referencing. Whole-brain T1-weighted structural images were acquired and segmented to determine gray matter, white matter and cerebrospinal fluid voxel tissue fractions. Water-referenced GABA measurements were fully corrected for tissue-dependent signal relaxation and water visibility effects. The cohort-wide coefficient of variation was 17% for the GABA + data and 29% for the MM-suppressed GABA data. The mean within-site coefficient of variation was 10% for the GABA + data and 19% for the MM-suppressed GABA data. Vendor differences contributed 53% to the total variance in the GABA + data, while the remaining variance was attributed to site- (11%) and participant-level (36%) effects. For the MM-suppressed data, 54% of the variance was attributed to site differences, while the remaining 46% was attributed to participant differences. Results from an exploratory analysis suggested that the vendor differences were related to the unsuppressed water signal acquisition. Discounting the observed vendor-specific effects, water-referenced GABA measurements exhibit similar levels of variance to creatine-referenced GABA measurements. It is concluded that quantification using internal tissue water referencing is a viable and reliable method for the quantification of in vivo GABA levels. ispartof: NEUROIMAGE vol:191 pages:537-548 ispartof: location:United States status: published

Developments in wearable technology have enabled researchers to continuously and objectively monitor various aspects and physiological domains of real-life including levels of physical activity, quality of sleep, and strength of circadian rhythm in many epidemiological and clinical studies. Current analytical practice is to summarize each of these three domains individually via a standard inventory of interpretable features, and explore individual associations between the features and clinical variables. However, the features often exhibit significant interaction and correlation both within and between domains. Integration of features across multiple domains remains methodologically challenging. To address this problem, we propose to use joint and individual variation explained (JIVE), a dimension reduction technique that efficiently deals with multivariate data representing multiple domains. In this paper, we review the most frequently used features to characterize the domains of physical activity, sleep, and circadian rhythniicity and illustrate the approach using wrist-worn actigraphy data from 198 participants of the Baltimore Longitudinal Study of Aging.

International audience; IMPORTANCE Biologic systems involved in the regulation of motor activity are intricately linked with other homeostatic systems such as sleep, feeding behavior, energy, and mood. Mobile monitoring technology (eg, actigraphy and ecological momentary assessment devices) allows the assessment of these multiple systems in real time. However, most clinical studies of mental disorders that use mobile devices have not focused on the dynamic associations between these systems.OBJECTIVES To examine the directional associations among motor activity, energy, mood, and sleep using mobile monitoring in a community-identified sample, and to evaluate whether these within-day associations differ between people with a history of bipolar or other mood disorders and controls without mood disorders.DESIGN, SETTING, AND PARTICIPANTS This study used a nested case-control design of 242 adults, a subsample of a community-based sample of adults. Probands were recruited by mail from the greater Washington, DC, metropolitan area from January 2005 to June 2013. Enrichment of the sample for mood disorders was provided by volunteers or referrals from the National Institutes of Health Clinical Center or by participants in the National Institute of Mental Health Mood and Anxiety Disorders Program. The inclusion criteria were the ability to speak English, availability to participate, and consent to contact at least 2 living first-degree relatives. Data analysis was performed from June 2013 through July 2018.MAIN OUTCOMES AND MEASURES Motor activity and sleep duration data were obtained from minute-to-minute activity counts from an actigraphy device worn on the nondominant wrist for 2 weeks. Mood and energy levels were assessed by subjective analogue ratings on the ecological momentary assessment (using a personal digital assistant) by participants 4 times per day for 2 weeks.RESULTS Of the total 242 participants, 92 (38.1%) were men and 150 (61.9%) were women, with a mean (SD) age of 48 (16.9) years. Among the participants, 54 (22.3%) had bipolar disorder (25 with bipolar I; 29 with bipolar II), 91 (37.6%) had major depressive disorder, and 97 (40.1%) were controls with no history of mood disorders. A unidirectional association was found between motor activity and subjective mood level (beta = -0.018, P = .04). Bidirectional associations were observed between motor activity (beta = 0.176; P = .03) and subjective energy level (beta = 0.027; P = .03) as well as between motor activity (beta = -0.027; P = .04) and sleep duration (beta = -0.154; P = .04). Greater cross-domain reactivity was observed in bipolar disorder across all outcomes, including motor activity, sleep, mood, and energy.CONCLUSIONS AND RELEVANCE These findings suggest that interventions focused on motor activity and energymay have greater efficacy than current approaches that target depressed mood; both active and passive tracking of multiple regulatory systems are important in designing therapeutic targets.

Alterations in 24-hour movement patterns, or circadian rest/activity rhythms (RARs), commonly occur with aging. Using linear mixed effects (LME) modeling, we examined associations of baseline RARs with longitudinal change in cognition. Participants (N=424; 47% male, baseline age 72.8±10.1 years) were from the Baltimore Longitudinal Study of Aging and completed 5.6±0.8 nights of wrist actigraphy at baseline. Tests of memory, executive function, attention, language, and visuospatial ability were administered at baseline and subsequent visits (3.7±1.7 years of follow-up in those with >1 visit (n=295)). In unadjusted random intercept and slope LME models, greater RAR stability predicted slower memory decline, and higher activity during participants’ least active 5 hours (L5) predicted slower decline in visuospatial ability. After covariate adjustment, higher activity in participants’ most active 10 hours (M10) and higher L5 predicted slower decline in visuospatial ability (p

Summary With the advent of continuous health monitoring with wearable devices, users now generate their unique streams of continuous data such as minute-level step counts or heartbeats. Summarizing these streams via scalar summaries often ignores the distributional nature of wearable data and almost unavoidably leads to the loss of critical information. We propose to capture the distributional nature of wearable data via user-specific quantile functions (QF) and use these QFs as predictors in scalar-on-quantile-function-regression (SOQFR). As an alternative approach, we also propose to represent QFs via user-specific L-moments, robust rank-based analogs of traditional moments, and use L-moments as predictors in SOQFR (SOQFR-L). These two approaches provide two mutually consistent interpretations: in terms of quantile levels by SOQFR and in terms of L-moments by SOQFR-L. We also demonstrate how to deal with multi-modal distributional data via Joint and Individual Variation Explained using L-moments. The proposed methods are illustrated in a study of association of digital gait biomarkers with cognitive function in Alzheimers disease. Our analysis shows that the proposed methods demonstrate higher predictive performance and attain much stronger associations with clinical cognitive scales compared to simple distributional summaries.

Study Objective To examine associations of personality dimensions and facets with insomnia symptoms in a community sample of older adults. Methods We studied 1049 participants aged 60–97 years in the Baltimore Longitudinal Study of Aging. Personality was assessed by the Revised NEO Personality Inventory (NEO-PI-R), and insomnia symptom severity was measured by the Women’s Health Initiative Insomnia Rating Scale (WHIIRS). Results Adjusting for demographic characteristics, higher neuroticism, lower conscientiousness, and lower extraversion were associated with greater insomnia symptom severity. These associations remained significant for neuroticism and conscientiousness when further adjusting for depressive symptoms and comorbidities. Higher scores on neuroticism facets Anxiety, Angry Hostility, and Depression, and lower scores on conscientiousness facets Competence, Order, and Achievement Striving and on agreeableness facet Altruism were associated with greater insomnia symptom severity in fully adjusted models. Results were similar among cognitively normal older adults (N = 966), except higher scores on extraversion facets Warmth and Assertiveness associated with lower insomnia symptom severity, and agreeableness facet Altruism was unassociated. Conclusion Among older adults, insomnia symptoms appear partially related to personality, with persons higher in neuroticism experiencing greater insomnia symptom severity, and those higher in conscientiousness experiencing lower insomnia symptom severity. Exploring facets of the Big-Five dimensions may provide additional insight regarding the etiology and resolution of sleep disturbance, and some of these associations may differ based on cognitive status. Future studies should investigate the hypothesis that sleep impairment mediates part of the association between specific personality traits and health-related outcomes.

Study Objectives To examine in a subsample at the screening phase of a clinical trial of a β-amyloid (Aβ) 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 Aβ-positive (Aβ+) and 33 Aβ-negative (Aβ−) 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 Aβ+ and Aβ− participants in standard sleep parameters or RAR metrics with our sample size. When we used novel FOSR methods, however, Aβ+ participants had lower activity levels than Aβ− 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). Aβ+ 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.

Study Objectives To compare sleep and circadian rest/activity rhythms (RARs), quantified by standard and novel actigraphic metrics, between controls and participants with mild cognitive impairment (MCI), and to examine the cross-sectional relationships between these measures and cognition. Methods Actigraphy data were collected in 179 older individuals (mean age = 72.6 years) with normal cognition (n = 153) and MCI (n = 26). Sleep parameters (e.g. sleep efficiency), and standard nonparametric RARs (e.g. interdaily stability) were generated. Functional principal component analysis (fPCA) was used to generate three novel RAR metrics (fPC1, fPC2, and fPC3). Cognitive composite scores reflecting episodic memory and executive function were derived using factor analysis. Regression models compared sleep and RAR parameters between diagnostic groups and their association with cognitive performance. Results Compared to controls, the MCI group exhibited lower levels of the standard RAR parameter: relative amplitude and fPC3—a novel RAR whereby lower scores reflected a lower rhythm peak, as well as greater nighttime activity and less activity in the morning. Across groups, several standard RAR parameters (e.g. interdaily stability) and fPC3 were associated with better episodic memory and executive function performance. Additionally, several standard RAR measures (e.g. relative amplitude) and the novel RAR measure fPC1 (reflecting the total volume of activity and rhythm strength) were associated with better executive function performance. Conclusions Individuals with MCI have altered circadian RARs compared to controls, including the novel RAR metric fPC3, reflecting greater nighttime activity and less activity in the morning compared to mean values. Additionally, these measures are significantly associated with cognitive performance.

By collecting data continuously over 24 hours, accelerometers and other wearable devices can provide novel insights into circadian rhythms and their relationship to human health. Existing approaches for analyzing diurnal patterns using these data, including the cosinor model and functional principal components analysis, have revealed and quantified population-level diurnal patterns, but considerable subject-level variability remained uncaptured in features such as wake/sleep times and activity intensity. This remaining informative variability could provide a better understanding of chronotypes, or behavioral manifestations of one's underlying 24-hour rhythm. Curve registration, or alignment, is a technique in functional data analysis that separates "vertical" variability in activity intensity from "horizontal" variability in time-dependent markers like wake and sleep times; this data-driven approach is well-suited to studying chronotypes using accelerometer data. We develop a parametric registration framework for 24-hour accelerometric rest-activity profiles represented as dichotomized into epoch-level states of activity or rest. Specifically, we estimate subject-specific piecewise linear time-warping functions parametrized with a small set of parameters. We apply this method to data from the Baltimore Longitudinal Study of Aging and illustrate how estimated parameters give a more flexible quantification of chronotypes compared to traditional approaches.