Unobtrusive Sensing Technology Detects Ecologically Valid Spatiotemporal Patterns of Daily Routines Distinctive to Persons with Mild Cognitive Impairment

Background The ability to capture people’s movement throughout their home is a powerful approach to inform spatiotemporal patterns of routines associated with cognitive impairment. The study estimated indoor room activities over 24 hours and investigated relationships between diurnal activity patterns and mild cognitive impairment (MCI). Methods 161 older adults (26 with MCI) living alone (age=78.9±9.2) were included from two study cohorts–the Oregon Center for Aging & Technology and the Minority Aging Research Study. Indoor room activities were measured by the number of trips made to rooms (bathroom, bedroom, kitchen, living room). Trips made to rooms (transitions) were detected using passive infrared motion sensors fixed on the walls for a month. Latent trajectory models were used to identify distinct diurnal patterns of room activities and characteristics associated with each trajectory. Results Latent trajectory models identified two diurnal patterns of bathroom usage (high; low usage). Participants with MCI were more likely to be in the high bathroom usage group that exhibited more trips to the bathroom than the low usage group (OR=4.1,95%CI [1.3-13.5],p=0.02). For kitchen activity, two diurnal patterns were identified (high; low activity). Participants with MCI were more likely to be in the high kitchen activity group that exhibited more transitions to the kitchen throughout the day and night than the low kitchen activity group (OR=3.2,95%CI [1.1-9.1],p=0.03). Conclusions The linkage between bathroom and kitchen activities with MCI may be the result of biological, health, and environmental factors in play. In-home, real-time unobtrusive-sensing offers a novel way of delineating cognitive health with chronologically-ordered movement across indoor locations.

A prospective cohort study on longitudinal trajectories of cognitive function after stroke

The study aimed to determine longitudinal trajectories of cognitive function during the first year after stroke. The Montreal Cognitive Assessment (MoCA) was used to screen cognitive function at 36–48 h, 3-months, and 12-months post-stroke. Individuals who shared similar trajectories were classified by applying the group-based trajectory models. Data from 94 patients were included in the analysis. Three cognitive functioning groups were identified by the trajectory models: high [14 patients (15%)], medium [58 (62%)] and low [22 (23%)]. For the high and medium groups, cognitive function improved at 12 months, but this did not occur in the low group. After age, sex and education matching to the normative MoCA from the Swedish population, 52 patients (55%) were found to be cognitively impaired at baseline, and few patients had recovered at 12 months. The impact on memory differs between cognitive functioning groups, whereas the impact on activities of daily living was not different. Patients with the poorest cognitive function did not improve at one-year poststroke and were prone to severe memory problems. These findings may help to increase focus on long-term rehabilitation plans for those patients, and more accurately assess their needs and difficulties experienced in daily living.

Distinct patterns of disease activity over time in patients with active SLE revealed using latent class trajectory models

Background Systemic lupus erythematosus (SLE) is a heterogeneous systemic autoimmune condition for which there are limited licensed therapies. Clinical trial design is challenging in SLE due at least in part to imperfect outcome measures. Improved understanding of how disease activity changes over time could inform future trial design. The aim of this study was to determine whether distinct trajectories of disease activity over time occur in patients with active SLE within a clinical trial setting and to identify factors associated with these trajectories. Methods Latent class trajectory models were fitted to a clinical trial dataset of a monoclonal antibody targeting CD22 (Epratuzumab) in patients with active SLE using the numerical BILAG-2004 score (nBILAG). The baseline characteristics of patients in each class and changes in prednisolone over time were identified. Exploratory PK-PD modelling was used to examine cumulative drug exposure in relation to latent class membership. Results Five trajectories of disease activity were identified, with 3 principal classes: non-responders (NR), slow responders (SR) and rapid-responders (RR). In both the SR and RR groups, significant changes in disease activity were evident within the first 90 days of the trial. The SR and RR patients had significantly higher baseline disease activity, exposure to epratuzumab and activity in specific BILAG domains, whilst NR had lower steroid use at baseline and less change in steroid dose early in the trial. Conclusions Longitudinal nBILAG scores reveal different trajectories of disease activity and may offer advantages over fixed endpoints. Corticosteroid use however remains an important confounder in lupus trials and can influence early response. Changes in disease activity and steroid dose early in the trial were associated with the overall disease activity trajectory, supporting the feasibility of performing adaptive trial designs in SLE.

Internal Variability Role on Estimating Sea Level Acceleration in Fremantle Tide Gauge Station

Low frequency internal signals bring challenges to signify the role of anthropogenic factors in sea level rise and to attain a certain accuracy in trend and acceleration estimations. Due to both spatially and temporally poor coverage of the relevant data sets, identification of internal variability patterns is not straightforward. In this study, the identification and the role of low frequency internal variability (decadal and multidecadal) in sea level change of Fremantle tide gauge station is analyzed using two climate indices, Pacific Decadal Oscillation (PDO) and Tripole Interdecadal Pacific Oscillation (TPI). It is shown that the multidecadal sea level variability is anticorrelated with corresponding components of climate indices in the Pacific Ocean, with correlation coefficients of −0.9 and −0.76 for TPI and PDO, respectively. The correlations are comparatively low on decadal time scale, −0.5 for both indices. This shows that internal variability on decadal and multidecadal scales affects the sea level variation in Fremantle unequally and thus, separate terms are required in trajectory models. To estimate trend and acceleration in Fremantle, three trajectory models are tested. The first model is a simple second-degree polynomial comprising trend and acceleration terms. Low passed PDO, representing decadal and interdecadal variabilities in Pacific Ocean, added to the first model to form the second model. For the third model, decomposed signals of decadal and multidecadal variability of TPI are added to the first model. In overall, TPI represents the low frequency internal variability slightly better than PDO for sea level variation in Fremantle. Although the estimated trends do not change significantly, the estimated accelerations varies for the three models. The accelerations estimated from the first and second models are statistically insignificant, 0.006 ± 0.012 mm yr−2 and 0.01 ± 0.01 mm yr−2, respectively, while this figure for the third model is 0.018 ± 0.011 mm yr−2. The outcome exemplifies the importance of modelling low frequency internal variability in acceleration estimations for sea level rise in regional scale.

Trajectories of antidepressant use before and after a suicide attempt among refugees and Swedish-born individuals: a cohort study

Background To identify key information regarding potential treatment differences in refugees and the host population, we aimed to investigate patterns (trajectories) of antidepressant use during 3 years before and after a suicide attempt in refugees, compared with Swedish-born. Association of the identified trajectory groups with individual characteristics were also investigated. Methods All 20–64-years-old refugees and Swedish-born individuals having specialised healthcare for suicide attempt during 2009–2015 (n = 62,442, 5.6% refugees) were followed 3 years before and after the index attempt. Trajectories of annual defined daily doses (DDDs) of antidepressants were analysed using group-based trajectory models. Associations between the identified trajectory groups and different covariates were estimated by chi2-tests and multinomial logistic regression. Results Among the four identified trajectory groups, antidepressant use was constantly low (≤15 DDDs) for 64.9% of refugees. A ‘low increasing’ group comprised 5.9% of refugees (60–260 annual DDDs before and 510–685 DDDs after index attempt). Two other trajectory groups had constant use at medium (110–190 DDDs) and high (630–765 DDDs) levels (22.5 and 6.6% of refugees, respectively). Method of suicide attempt and any use of psychotropic drugs during the year before index attempt discriminated between refugees’ trajectory groups. The patterns and composition of the trajectory groups and their association, discriminated with different covariates, were fairly similar among refugees and Swedish-born, with the exception of previous hypnotic and sedative drug use being more important in refugees. Conclusions Despite previous reports on refugees being undertreated regarding psychiatric healthcare, no major differences in antidepressant treatment between refugees and Swedish-born suicide attempters were found.

Using linear and natural cubic splines, SITAR, and latent trajectory models to characterise nonlinear longitudinal growth trajectories in cohort studies

Longitudinal data analysis can improve our understanding of the influences on health trajectories across the life-course. There are a variety of statistical models which can be used, and their fitting and interpretation can be complex, particularly where there is a nonlinear trajectory. This paper provides a guide to describing nonlinear growth trajectories for repeatedly measured continuous outcomes using linear mixed-effects (LME) models with linear splines and natural cubic splines, nonlinear mixed effects Super Imposition by Translation and Rotation (SITAR) models, and latent trajectory models. The underlying model for each of the four approaches, the similarities and differences between models, and their advantages and disadvantages are described. Their applications and correct interpretation are illustrated by analysing repeated bone mass measures across three cohort studies with 8,500 individuals and 37,000 measurements covering ages 5-40 years. Linear and natural cubic spline LME models and SITAR provided similar descriptions of the mean bone growth trajectory and growth velocity, and the sex differences in growth patterns. Latent trajectory models identified up to four subgroups of individuals with distinct trajectories during adolescence and similar trajectories in childhood and adulthood. Recommendations for choosing a modelling approach are provided along with a discussion and signposting on further modelling extensions for analysing trajectory exposures and outcomes, and multiple cohorts. In summary, we present a resource for characterising nonlinear longitudinal growth trajectories, that could be adapted for other complex traits. Scripts and synthetic datasets are provided so readers can replicate trajectory modelling and visualisation using the open-source R software.

Trajectory Based Market Models for Two Stocks

This paper studies the explicit calculation of the set of superhedging (and underhedging) portfolios where one asset is used to superhedge another in a discrete time setting. A general operational framework is proposed and trajectory models are defined based on a class of investors characterized by how they operate on financial data leading to potential portfolio rebalances. Trajectory market models will be specified by a trajectory set and a set of portfolios. Beginning with observing charts in an operationally prescribed manner, our trajectory sets will be constructed by moving forward recursively, while our superhedging portfolios are computed through a backwards recursion process involving a convex hull algorithm. The models proposed in this thesis allow for an arbitrary number of stocks and arbitrary choice of numeraire. Although price bounds, V 0 (X0, X2 ,M) ≤ V 0(X0, X2 ,M), will never yield a market misprice, our models will allow an investor to determine the amount of risk associated with an initial investment v.