Multiple Imputation with Factor Scores: A Practical Approach for Handling Simultaneous Missingness Across Items in Longitudinal Designs.

Missingness in intensive longitudinal data triggered by latent factors constitute one type of nonignorable missingness that can generate simultaneous missingness across multiple items on each measurement occasion. To address this issue, we propose a multiple imputation (MI) strategy called MI-FS, which incorporates factor scores, lag/lead variables, and missing data indicators into the imputation model. In the context of process factor analysis (PFA), we conducted a Monte Carlo simulation study to compare the performance of MI-FS to listwise deletion (LD), MI with manifest variables (MI-MV, which implements MI on both dependent variables and covariates), and partial MI with MVs (PMI-MV, which implements MI on covariates and handles missing dependent variables via full-information maximum likelihood) under different conditions. Across conditions, we found MI-based methods overall outperformed the LD; the MI-FS approach yielded lower root mean square errors (RMSEs) and higher coverage rates for auto-regression (AR) parameters compared to MI-MV; and the PMI-MV and MI-MV approaches yielded higher coverage rates for most parameters except AR parameters compared to MI-FS. These approaches were also compared using an empirical example investigating the relationships between negative affect and perceived stress over time. Recommendations on when and how to incorporate factor scores into MI processes were discussed.

Momentary PERMA: An Adapted Measurement Tool for Studying Well-Being in Daily Life.

PERMA is a multidimensional framework that explains well-being through five hedonic and eudaimonic psychological elements-Positive emotions, Engagement, Relationships, Meaning, Accomplishment. Soon after the PERMA framework was proposed, PERMA-Profiler was introduced as a validated assessment tool for measuring these five elements of well-being from a global perspective. The current study aimed to shed further light onto the measurement of PERMA elements, extending it beyond global evaluations, to daily life assessments and the examination of individual differences in their dynamic characteristics. We introduce mPERMA (momentary PERMA), as an EMA-adapted version of the PERMA-Profiler measure, to assess well-being in daily life. Using data collected in an Ecological Momentary Assessment (EMA) study (N = 160), we first demonstrate the factor structure of mPERMA through a multilevel factor analysis and next examine within-person means and the dynamics of change (e.g., intra-individual variability) in the PERMA elements. Findings revealed that mPERMA displays convergent validity with two global measures of hedonic and eudaimonic well-being, namely Flourishing and Subjective Well-Being. Moreover, dynamical characteristics of the five elements of well-being measured over time, map onto their corresponding hedonic or eudaimonic global measures of well-being. Results of this paper present how dynamical features of well-being in daily life provide novel insights into predicting global well-being. Supplementary Information: The online version contains supplementary material available at 10.1007/s10902-023-00684-w.

Quantifying Evidence for-and against-Granger Causality with Bayes Factors.

Testing for Granger causality relies on estimating the capacity of dynamics in one time series to forecast dynamics in another. The canonical test for such temporal predictive causality is based on fitting multivariate time series models and is cast in the classical null hypothesis testing framework. In this framework, we are limited to rejecting the null hypothesis or failing to reject the null - we can never validly accept the null hypothesis of no Granger causality. This is poorly suited for many common purposes, including evidence integration, feature selection, and other cases where it is useful to express evidence against, rather than for, the existence of an association. Here we derive and implement the Bayes factor for Granger causality in a multilevel modeling framework. This Bayes factor summarizes information in the data in terms of a continuously scaled evidence ratio between the presence of Granger causality and its absence. We also introduce this procedure for the multilevel generalization of Granger causality testing. This facilitates inference when information is scarce or noisy or if we are interested primarily in population-level trends. We illustrate our approach with an application on exploring causal relationships in affect using a daily life study.

Fitting Bayesian Stochastic Differential Equation Models with Mixed Effects through a Filtering Approach.

Recent advances in technology contribute to a fast-growing number of studies utilizing intensive longitudinal data, and call for more flexible methods to address the demands that come with them. One issue that arises from collecting longitudinal data from multiple units in time is nested data, where the variability observed in such data is a mixture of within-unit changes and between-unit differences. This article aims to provide a model-fitting approach that simultaneously models the within-unit changes with differential equation models and accounts for between-unit differences with mixed effects. This approach combines a variant of the Kalman filter, the continuous-discrete extended Kalman filter (CDEKF), and the Markov chain Monte Carlo method often employed in the Bayesian framework through the platform Stan. At the same time, it utilizes Stan's functionality of numerical solvers for the implementation of CDEKF. For an empirical illustration, we applied this method in the context of differential equation models to an empirical dataset to explore the physiological dynamics and co-regulation between couples.

Are paranoid ideation and hallucination spectrum experiences differently associated with affect dynamics? A continuous-time modeling approach.

Psychotic experiences have been associated with distortions in affective functioning, including aberrancies in affect dynamics. However, it remains unclear whether the two principal symptom dimensions of psychosis, namely paranoid ideation and hallucination spectrum experiences, are differently associated with affect dynamics, and whether associations hold after statistically controlling for depressive symptoms. We investigate this by using a novel statistical approach, the hierarchical Ornstein-Uhlenbeck (OU) process model. This is a continuous-time stochastic differential equations model in a Bayesian framework that describes dynamics in affective valence and arousal via three core parameters: attractor point, variability, and attractor strength. In a community sample with varying levels of psychotic experiences (n = 116), we measured affective valence and arousal 10 times per day for 7 days, using the experience-sampling method. We found-while statistically controlling for depressive symptoms-credible between-subjects associations between paranoid ideation and attractor points of negative valence and high arousal. We also found a credible positive association between hallucination spectrum experiences and arousal variability. Limited evidence emerged for small associations between paranoid ideation and high valence variability as well as between paranoid ideation and high attractor strengths of valence and arousal. Hallucination spectrum experiences showed some evidence for a small association with high arousal attractor points. The detailed picture of affect dynamics provided by the OU model reveals different cross-sectional affective profiles associated with paranoid ideation versus hallucination spectrum experiences that suggest different affective mechanisms of their formation and maintenance. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Accounting for retest effects in cognitive testing with the Bayesian double exponential model via intensive measurement burst designs.

Monitoring early changes in cognitive performance is useful for studying cognitive aging as well as for detecting early markers of neurodegenerative diseases. Repeated evaluation of cognition via a measurement burst design can accomplish this goal. In such design participants complete brief evaluations of cognition, multiple times per day for several days, and ideally, repeat the process once or twice a year. However, long-term cognitive change in such repeated assessments can be masked by short-term within-person variability and retest learning (practice) effects. In this paper, we show how a Bayesian double exponential model can account for retest gains across measurement bursts, as well as warm-up effects within a burst, while quantifying change across bursts in peak performance. We also highlight how this approach allows for the inclusion of person-level predictors and draw intuitive inferences on cognitive change with Bayesian posterior probabilities. We use older adults' performance on cognitive tasks of processing speed and spatial working memory to demonstrate how individual differences in peak performance and change can be related to predictors of aging such as biological age and mild cognitive impairment status.

Fitting Multilevel Vector Autoregressive Models in Stan, JAGS, and Mplus.

The influx of intensive longitudinal data creates a pressing need for complex modeling tools that help enrich our understanding of how individuals change over time. Multilevel vector autoregressive (mlVAR) models allow for simultaneous evaluations of reciprocal linkages between dynamic processes and individual differences, and have gained increased recognition in recent years. High-dimensional and other complex variations of mlVAR models, though often computationally intractable in the frequentist framework, can be readily handled using Markov chain Monte Carlo techniques in a Bayesian framework. However, researchers in social science fields may be unfamiliar with ways to capitalize on recent developments in Bayesian software programs. In this paper, we provide step-by-step illustrations and comparisons of options to fit Bayesian mlVAR models using Stan, JAGS and Mplus, supplemented with a Monte Carlo simulation study. An empirical example is used to demonstrate the utility of mlVAR models in studying intra- and inter-individual variations in affective dynamics.

Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates.

In this paper, we present and evaluate a novel Bayesian regime-switching zero-inflated multilevel Poisson (RS-ZIMLP) regression model for forecasting alcohol use dynamics. The model partitions individuals' data into two phases, known as regimes, with: (1) a zero-inflation regime that is used to accommodate high instances of zeros (non-drinking) and (2) a multilevel Poisson regression regime in which variations in individuals' log-transformed average rates of alcohol use are captured by means of an autoregressive process with exogenous predictors and a person-specific intercept. The times at which individuals are in each regime are unknown, but may be estimated from the data. We assume that the regime indicator follows a first-order Markov process as related to exogenous predictors of interest. The forecast performance of the proposed model was evaluated using a Monte Carlo simulation study and further demonstrated using substance use and spatial covariate data from the Colorado Online Twin Study (CoTwins). Results showed that the proposed model yielded better forecast performance compared to a baseline model which predicted all cases as non-drinking and a reduced ZIMLP model without the RS structure, as indicated by higher AUC (the area under the receiver operating characteristic (ROC) curve) scores, and lower mean absolute errors (MAEs) and root-mean-square errors (RMSEs). The improvements in forecast performance were even more pronounced when we limited the comparisons to participants who showed at least one instance of transition to drinking.

Decision-Making Processes Related to Perseveration Are Indirectly Associated With Weight Status in Children Through Laboratory-Assessed Energy Intake.

Decision-making contributes to what and how much we consume, and deficits in decision-making have been associated with increased weight status in children. Nevertheless, the relationships between cognitive and affective processes underlying decision-making (i.e., decision-making processes) and laboratory food intake are unclear. We used data from a four-session, within-subjects laboratory study to investigate the relationships between decision-making processes, food intake, and weight status in 70 children 7-to-11-years-old. Decision-making was assessed with the Hungry Donkey Task (HDT), a child-friendly task where children make selections with unknown reward outcomes. Food intake was measured with three paradigms: (1) a standard ad libitum meal, (2) an eating in the absence of hunger (EAH) protocol, and (3) a palatable buffet meal. Individual differences related to decision-making processes during the HDT were quantified with a reinforcement learning model. Path analyses were used to test whether decision-making processes that contribute to children's (a) expected value of a choice and (b) tendency to perseverate (i.e., repeatedly make the same choice) were indirectly associated with weight status through their effects on intake (kcal). Results revealed that increases in the tendency to perseverate after a gain outcome were positively associated with intake at all three paradigms and indirectly associated with higher weight status through intake at both the standard and buffet meals. Increases in the tendency to perseverate after a loss outcome were positively associated with EAH, but only in children whose tendency to perseverate persistedacross trials. Results suggest that decision-making processes that shape children's tendencies to repeat a behavior (i.e., perseverate) are related to laboratory energy intake across multiple eating paradigms. Children who are more likely to repeat a choice after a positive outcome have a tendency to eat more at laboratory meals. If this generalizes to contexts outside the laboratory, these children may be susceptible to obesity. By using a reinforcement learning model not previously applied to the study of eating behaviors, this study elucidated potential determinants of excess energy intake in children, which may be useful for the development of childhood obesity interventions.

Capturing emotion coherence in daily life: Using ambulatory physiology measures and ecological momentary assessments to examine within-person associations and individual differences.

While emotion coherence has long been theorized to be a core feature of emotion, to date, studies examining response coherence have been conducted in laboratory settings. The present study used a combined approach of ambulatory physiology measures and ecological momentary assessment conducted over a 4-week period to examine the extent to which emotional experience and physiology show coherence in daily life within-persons; and whether individual differences in response coherence are associated with between-person differences in well-being, negative emotionality, and gender. Results revealed that, on average, individuals exhibited coherence between subjective experience and physiology of emotion, but that there was substantial between-person variation in coherence in daily life. Exploratory analyses revealed no credible link between levels of response coherence and well-being, negative emotionality, or gender. Findings contribute to the literature by demonstrating a novel methodological approach to measuring coherence in daily life and supporting the generalizability of coherence to ecologically valid contexts.

Low Resting Energy Expenditure Is Associated with High Gestational Weight Gain Only When Resting Energy Expenditure Fluctuates.

Resting energy expenditure (REE) may be useful for individualizing energy intake (EI) and physical activity (PA) goals, and in turn, regulating gestational weight gain (GWG). Limited research, however, has examined the association between REE and GWG. This study examined (1) change in REE from 14 to 28 gestation, (2) time-varying associations between REE and GWG, and (3) EI and PA patterns during the weeks when REE and GWG were significantly associated. Pregnant women with overweight/obesity (N = 27) participating in the Healthy Mom Zone study completed weekly point estimates of EI (back-calculation), PA (wrist-worn activity monitor), REE (mobile metabolism device), and weight (Wi-Fi scale) from 14 to 28 weeks gestation. Analyses included descriptives and time-varying effect modeling. REE fluctuated, increasing on average from 14 to 28 weeks gestation, but decreased at gestational weeks 17, 20, 21, 23, 26, and 28. Most women increased in REE; however there was large between-person variability in the amount of change. Associations between REE and GWG were small but time-varying; low REE was associated with high GWG between gestational weeks 25 to 28 when there was observably larger fluctuation in REE. Moreover, over half of the women were categorized as having excessive EI and most as low active during this time. EI needs may be overestimated and PA needs may be underestimated when REE is fluctuating, which may increase the risk for high second trimester GWG. Researchers should consider the role of REE to inform EI and PA goals to regulate GWG.

GPS2space: An Open-source Python Library for Spatial Measure Extraction from GPS Data.

Global Positioning System (GPS) data have become one of the routine data streams collected by wearable devices, cell phones, and social media platforms in this digital age. Such data provide research opportunities in that they may provide contextual information to elucidate where, when, and why individuals engage in and sustain particular behavioral patterns. However, raw GPS data consisting of densely sampled time series of latitude and longitude coordinate pairs do not readily convey meaningful information concerning intra-individual dynamics and inter-individual differences; substantial data processing is required. Raw GPS data need to be integrated into a Geographic Information System (GIS) and analyzed, from which the mobility and activity patterns of individuals can be derived, a process that is unfamiliar to many behavioral scientists. In this tutorial article, we introduced GPS2space, a free and open-source Python library that we developed to facilitate the processing of GPS data, integration with GIS to derive distances from landmarks of interest, as well as extraction of two spatial features: activity space of individuals and shared space between individuals, such as members of the same family. We demonstrated functions available in the library using data from the Colorado Online Twin Study to explore seasonal and age-related changes in individuals' activity space and twin siblings' shared space, as well as gender, zygosity and baseline age-related differences in their initial levels and/or changes over time. We concluded with discussions of other potential usages, caveats, and future developments of GPS2space.

Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology.

OBJECTIVE: One of the promises of the experience sampling methodology (ESM) is that a statistical analysis of an individual's emotions, cognitions and behaviors in everyday-life could be used to identify relevant treatment targets. A requisite for clinical implementation is that outcomes of such person-specific time-series analyses are not wholly contingent on the researcher performing them. METHODS: To evaluate this, we crowdsourced the analysis of one individual patient's ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment. RESULTS: Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics and rationale for selecting targets. Most teams did include a type of vector autoregressive model, examining relations between symptoms over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one recommendation was similar: both the number (0-16) and nature of selected targets varied widely. CONCLUSION: This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key conceptual and methodological issues that need to be addressed in moving towards clinical implementation.

A Bayesian Vector Autoregressive Model with Nonignorable Missingness in Dependent Variables and Covariates: Development, Evaluation, and Application to Family Processes.

Intensive longitudinal designs involving repeated assessments of constructs often face the problems of nonignorable attrition and selected omission of responses on particular occasions. However, time series models, such as vector autoregressive (VAR) models, are often fit to these data without consideration of nonignorable missingness. We introduce a Bayesian model that simultaneously represents the over-time dependencies in multivariate, multiple-subject time series data via a VAR model, and possible ignorable and nonignorable missingness in the data. We provide software code for implementing this model with application to an empirical data set. Moreover, simulation results comparing the joint approach with two-step multiple imputation procedures are included to shed light on the relative strengths and weaknesses of these approaches in practical data analytic scenarios.

Low-Burden Mobile Monitoring, Intervention, and Real-Time Analysis Using the Wear-IT Framework: Example and Usability Study.

BACKGROUND: Mobile health (mHealth) methods often rely on active input from participants, for example, in the form of self-report questionnaires delivered via web or smartphone, to measure health and behavioral indicators and deliver interventions in everyday life settings. For short-term studies or interventions, these techniques are deployed intensively, causing nontrivial participant burden. For cases where the goal is long-term maintenance, limited infrastructure exists to balance information needs with participant constraints. Yet, the increasing precision of passive sensors such as wearable physiology monitors, smartphone-based location history, and internet-of-things devices, in combination with statistical feature selection and adaptive interventions, have begun to make such things possible. OBJECTIVE: In this paper, we introduced Wear-IT, a smartphone app and cloud framework intended to begin addressing current limitations by allowing researchers to leverage commodity electronics and real-time decision making to optimize the amount of useful data collected while minimizing participant burden. METHODS: The Wear-IT framework uses real-time decision making to find more optimal tradeoffs between the utility of data collected and the burden placed on participants. Wear-IT integrates a variety of consumer-grade sensors and provides adaptive, personalized, and low-burden monitoring and intervention. Proof of concept examples are illustrated using artificial data. The results of qualitative interviews with users are provided. RESULTS: Participants provided positive feedback about the ease of use of studies conducted using the Wear-IT framework. Users expressed positivity about their overall experience with the framework and its utility for balancing burden and excitement about future studies that real-time processing will enable. CONCLUSIONS: The Wear-IT framework uses a combination of passive monitoring, real-time processing, and adaptive assessment and intervention to provide a balance between high-quality data collection and low participant burden. The framework presents an opportunity to deploy adaptive assessment and intervention designs that use real-time processing and provides a platform to study and overcome the challenges of long-term mHealth intervention.

Cortisol dynamics in depression: Application of a continuous-time process model.

BACKGROUND: The temporal dynamics of cortisol may be altered in depression. Optimally studying these dynamics in daily life requires specific analytical methods. We used a continuous-time multilevel process model to study set point (rhythm-corrected mean), variability around this set point, and regulation strength (speed with which cortisol levels regulate back to the set point after any perturbation). We examined the generalizability of the parameters across two data sets with different sampling and assay methods, and the hypothesis that regulation strength, but not set point or variability thereof, would be altered in depressed, compared to non-depressed individuals. METHODS: The first data set is a general population sample of female twins (n = 523), of which 21 were depressed, with saliva samples collected 10 times a day for 5 days. The second data set consists of pair-matched clinically depressed and non-depressed individuals (n = 30), who collected saliva samples 3 times a day for 30 days. Set point, regulation strength and variability were examined using a Bayesian multilevel Ornstein-Uhlenbeck (OU) process model. They were first compared between samples, and thereafter assessed within samples in relation to depression. RESULTS: Set point and variability of salivary cortisol were twice as high in the female twin sample, compared to the pair-matched sample. The ratio between set point and variability, as well as regulation strength, which are relative measures and therefore less affected by the specific assay method, were similar across samples. The average regulation strength was high; after an increase in cortisol, cortisol levels would decrease by 63 % after 10 min, and by 95 % after 30 min, but depressed individuals of the pair-matched sample displayed an even faster regulation strength. CONCLUSIONS: The OU process model recovered similar cortisol dynamics for the relative parameters of the two data sets. The results suggest that regulation strength is increased in depressed individuals. We recommend the presented methodology for future studies and call for replications with more diverse depressed populations.

Individualized Modeling to Distinguish Between High and Low Arousal States Using Physiological Data.

With wearable, relatively unobtrusive health monitors and smartphone sensors, it is increasingly easy to collect continuously streaming physiological data in a passive mode without placing much burden on participants. At the same time, smartphones provide the ability to survey participants to provide "ground-truth" reporting on psychological states, although this comes at an increased cost in participant burden. In this paper, we examined how analytical approaches from the field of machine learning could allow us to distill the collected physiological data into actionable decision rules about each individual's psychological state, with the eventual goal of identifying important psychological states (e.g., risk moments) without the need for ongoing burdensome active assessment (e.g., self-report). As a first step towards this goal, we compared two methods: (1) a k-nearest neighbor classifier that uses dynamic time warping distance, and (2) a random forests classifier to predict low and high states of affective arousal states based on features extracted using the tsfresh python package. Then, we compared random-forest-based predictive models tailored for the individual with individual-general models. Results showed that the individual-specific model outperformed the general one. Our results support the feasibility of using passively collected wearable data to predict psychological states, suggesting that by relying on both types of data, the active collection can be reduced or eliminated.

Living Well With Dementia: Factors Associated With Nursing Home Residents' Affect Balance.

Well-being is an important outcome for people with dementia. The current study is a secondary analysis of baseline data from an ongoing pragmatic trial. Affect balance, the ratio of positive to negative affect, was used as a measure of well-being, and factors related to it were examined in a sample of 325 nursing home residents. Measures of staff interaction during caregiving, staff knowledge of person-centered approaches for dementia care, staff hours of care, the physical environment, person-centered policies, resident function, and quality of life were obtained using direct observation, staff interview, and medical chart review. The results of the quantile multivariable regression analysis indicated that positive staff interaction and higher resident function were significantly associated with higher affect balance after controlling for other variables. The findings have heuristic value for the development of conceptual frameworks that focus on meaningful outcomes for residents with dementia and future research. [Research in Gerontological Nursing, 13(1), 21-30.].

dynr.mi: An R Program for Multiple Imputation in Dynamic Modeling.

Assessing several individuals intensively over time yields intensive longitudinal data (ILD). Even though ILD provide rich information, they also bring other data analytic challenges. One of these is the increased occurrence of missingness with increased study length, possibly under non-ignorable missingness scenarios. Multiple imputation (MI) handles missing data by creating several imputed data sets, and pooling the estimation results across imputed data sets to yield final estimates for inferential purposes. In this article, we introduce dynr.mi(), a function in the R package, Dynamic Modeling in R (dynr). The package dynr provides a suite of fast and accessible functions for estimating and visualizing the results from fitting linear and nonlinear dynamic systems models in discrete as well as continuous time. By integrating the estimation functions in dynr and the MI procedures available from the R package, Multivariate Imputation by Chained Equations (MICE), the dynr.mi() routine is designed to handle possibly non-ignorable missingness in the dependent variables and/or covariates in a user-specified dynamic systems model via MI, with convergence diagnostic check. We utilized dynr.mi() to examine, in the context of a vector autoregressive model, the relationships among individuals' ambulatory physiological measures, and self-report affect valence and arousal. The results from MI were compared to those from listwise deletion of entries with missingness in the covariates. When we determined the number of iterations based on the convergence diagnostics available from dynr.mi(), differences in the statistical significance of the covariate parameters were observed between the listwise deletion and MI approaches. These results underscore the importance of considering diagnostic information in the implementation of MI procedures.

A Novel Hierarchical LATER Process Model: Evaluating Latent Sources of Variation in Reaction Times of Adult Daily Smokers.

Reaction time data from cognitive tasks continue to be a key way to assess decision-making in various contexts to better understand addiction. The goal of this paper is twofold: to introduce a nuanced modeling approach for reaction time data and to demonstrate the novel insights it can provide into the decision processes of nicotine-dependent individuals in different contexts. We focus on the Linear Approach to Threshold with Ergodic Rate (LATER) model, which is a cognitive process model that describes reaction time data in terms of two distinct aspects of cognitive functioning: speed of information accumulation ("accretion") and threshold amount of information needed prior to execution ("caution"). We introduce a novel hierarchical extension to the LATER model to simultaneously account for differences across persons and experimental conditions, both in the accretion and caution parameters. This approach allows for the inclusion of person-specific predictor variables to explain between-person variation in terms of accretion and caution together with condition-specific predictors to model experimental condition manipulations. To highlight the usefulness of this model, we analyze reaction time data from a study on adult daily cigarette smokers. Participants performed a monetary incentivized Go/No-Go task during two testing sessions, once while following their typical smoking patterns and again following 12 h of verified smoking abstinence. Our main results suggest that regardless of trial type, smokers in a period of abstinence have faster accretion rates, and lower caution thresholds relative to smoking as usual.