Subgrouping with Chain Graphical VAR Models.

Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.

Physical activity and menopausal symptoms: evaluating the contribution of obesity, fitness, and ambient air pollution status.

OBJECTIVE: The menopausal transition is accompanied by transient symptoms that have been linked to subclinical cardiovascular disease (CVD); CVD has also been linked to air pollution. Physical activity (PA) reduces CVD, improves body composition, and can reduce menopausal symptoms. The purpose of this study was to assess the links between PA and menopausal symptoms and whether obesity, fitness, and air pollution status play a role in this relationship. METHODS: Women (40-60 y; N = 243; mean [SD] age, 47.8 [5.6] y) from areas with high versus low air pollution enrolled in the Healthy Aging in Industrial Environment Program 4 prospective cohort study completed psychological, cardiorespiratory fitness, body composition, and menopausal status screening followed by a 14-day prospective assessment of menopausal symptoms (Menopause Rating Scale) using a mobile application. Daily PA was assessed objectively across 14 days via Fitbit Charge 3 monitor. General linear mixed models were conducted and controlled for age, menopausal status, day in the study, wear time, and neuroticism. RESULTS: Peri/postmenopausal women ( ß = 0.43, P < 0.001) and those residing in a high-air-pollution environment ( ß = 0.45, P < 0.05) reported more somatovegetative symptoms. Hot flashes alone were associated with peri/postmenopausal status ( ß = 0.45, P < 0.001), and for women residing in a high-air-pollution environment, lower reporting of hot flashes was observed on days when a woman was more physically active than usual ( ß = -0.15, P < 0.001). No associations were found for cardiorespiratory fitness and visceral fat with any of the symptoms. CONCLUSIONS: PA may enhance resilience to hot flashes, especially when residing in high-air-pollution environments where we also observed higher reporting of somatovegetative menopausal symptoms.

From intention to behavior: Within- and between-person moderators of the relationship between intention and physical activity.

Intention is a proximal predictor of behavior in many theories of behavior change, but intentions to be physically active do not always translate to actual physical activity. Little research has examined intensive longitudinal changes in physical activity and corresponding within-person moderators needed to elucidate the mechanisms, hurdles, and facilitators of individuals' everyday physical activity behaviors. The present study set out to evaluate the possible moderators of the intention-physical activity relationship across within-person and between-person levels, including cross-level interactions. Data comprise the first intensive measurement burst (14 days) of the longitudinal prospective Healthy Aging in Industrial Environment (HAIE) study, with N = 1135 participants (N = 10,030 person-days), aged 18-65. Physical activity was operationalized as step counts measured objectively using Fitbit Charge 3/4 fitness monitor. Intention, barriers to physical activity, and social support for physical activity were measured daily via smartphone surveys. Stable characteristics, i.e., physical activity habit and exercise identity, were measured using an online questionnaire. A multilevel moderation regression model with Bayesian estimator was fitted. At the within-person level, the relation between intention and steps was weaker on days when barriers were more severe than usual for a given person (Estimate = -0.267; CI95 = [-0.340, -0.196]) and social support was below average for a given person (Est = 0.143; CI95 = [0.023, 0.262]). Additionally, the daily intention-behavior relationship was stronger for people with lower average severity of barriers (Est = -0.153; CI95 = [-0.268, -0.052]), higher exercise identity (Est = 0.300; CI95 = [0.047, 0.546]), men (Est = -1.294, CI95 = [-1.854, -0.707]), and older individuals (Est = 0.042, CI95 = [0.017, 0.064]). At the between-person level, only physical activity habit strengthened the intention-behavior link (Est = 0.794; CI95 = [0.090, 1.486]). Our results underscore the need to separate the between-person differences from the within-person fluctuations to better understand the individual dynamics in physical activity behaviors. Personalized interventions aimed at helping individuals translate intentions to actual physical activity could be tailored and become more intensive when there is a higher risk of intention-behavior gap on a given day for a specific individual (i.e., a day with more severe barriers and less social support), by increasing the dosage or deploying more precisely targeted intervention strategies and components. In addition, interventionists should take gender and age into account when tailoring everyday strategies to help individuals act on their intentions.

Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data.

Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals' affective dynamics and urge.

Estimation of nonlinear mixed-effects continuous-time models using the continuous-discrete extended Kalman filter.

Many intensive longitudinal measurements are collected at irregularly spaced time intervals, and involve complex, possibly nonlinear and heterogeneous patterns of change. Effective modelling of such change processes requires continuous-time differential equation models that may be nonlinear and include mixed effects in the parameters. One approach of fitting such models is to define random effect variables as additional latent variables in a stochastic differential equation (SDE) model of choice, and use estimation algorithms designed for fitting SDE models, such as the continuous-discrete extended Kalman filter (CDEKF) approach implemented in the dynr R package, to estimate the random effect variables as latent variables. However, this approach's efficacy and identification constraints in handling mixed-effects SDE models have not been investigated. In the current study, we analytically inspect the identification constraints of using the CDEKF approach to fit nonlinear mixed-effects SDE models; extend a published model of emotions to a nonlinear mixed-effects SDE model as an example, and fit it to a set of irregularly spaced ecological momentary assessment data; and evaluate the feasibility of the proposed approach to fit the model through a Monte Carlo simulation study. Results show that the proposed approach produces reasonable parameter and standard error estimates when some identification constraint is met. We address the effects of sample size, process noise variance, and data spacing conditions on estimation results.

Evaluating Discrete Time Methods for Subgrouping Continuous Processes.

Rapid developments over the last several decades have brought increased focus and attention to the role of time scales and heterogeneity in the modeling of human processes. To address these emerging questions, subgrouping methods developed in the discrete-time framework-such as the vector autoregression (VAR)-have undergone widespread development to identify shared nomothetic trends from idiographic modeling results. Given the dependence of VAR-based parameters on the measurement intervals of the data, we sought to clarify the strengths and limitations of these methods in recovering subgroup dynamics under different measurement intervals. Building on the work of Molenaar and collaborators for subgrouping individual time-series by means of the subgrouped chain graphical VAR (scgVAR) and the subgrouping option in the group iterative multiple model estimation (S-GIMME), we present results from a Monte Carlo study aimed at addressing the implications of identifying subgroups using these discrete-time methods when applied to continuous-time data. Results indicate that discrete-time subgrouping methods perform well at recovering true subgroups when the measurement intervals are large enough to capture the full range of a system's dynamics, either via lagged or contemporaneous effects. Further implications and limitations are discussed therein.

Automated measurement of infant and mother Duchenne facial expressions in the Face-to-Face/Still-Face.

Although still-face effects are well-studied, little is known about the degree to which the Face-to-Face/Still-Face (FFSF) is associated with the production of intense affective displays. Duchenne smiling expresses more intense positive affect than non-Duchenne smiling, while Duchenne cry-faces express more intense negative affect than non-Duchenne cry-faces. Forty 4-month-old infants and their mothers completed the FFSF, and key affect-indexing facial Action Units (AUs) were coded by expert Facial Action Coding System coders for the first 30 s of each FFSF episode. Computer vision software, automated facial affect recognition (AFAR), identified AUs for the entire 2-min episodes. Expert coding and AFAR produced similar infant and mother Duchenne and non-Duchenne FFSF effects, highlighting the convergent validity of automated measurement. Substantive AFAR analyses indicated that both infant Duchenne and non-Duchenne smiling declined from the FF to the SF, but only Duchenne smiling increased from the SF to the RE. In similar fashion, the magnitude of mother Duchenne smiling changes over the FFSF were 2-4 times greater than non-Duchenne smiling changes. Duchenne expressions appear to be a sensitive index of intense infant and mother affective valence that are accessible to automated measurement and may be a target for future FFSF research.

PTSD Symptom Cluster Severity Predicts Momentary Emotion Dynamics During Couple Conversations.

This study investigated the associations between momentary emotion dynamics and posttraumatic stress disorder (PTSD) symptoms. Using a sample of 61 couples (N = 122 individuals) in which all individuals were trauma exposed and at least one partner screened positive for PTSD, we examined the intra- and interpersonal regulation of vocally encoded emotional arousal (fundamental frequency [f0]) and how these momentary emotion regulatory patterns relate to specific PTSD symptoms during two couple conversations: one designed to elicit conflict and one to elicit intimacy. PTSD symptoms were assessed using a gold standard clinical interview. In both conversations, higher reexperiencing symptoms were associated with greater emotional inertia (i.e., more resistance to change in emotional state following deviation from one's emotional equilibrium), and higher avoidance symptoms were associated with less emotional inertia (i.e., quicker return to emotional equilibrium). In the intimacy conversations, individuals also responded to their partners' arousal. Furthermore, individuals whose partners exhibited higher emotional numbing symptoms exhibited more emotional inertia, suggesting that emotion regulation may be a function of both one's own and one's partner's PTSD symptoms. Attending to the interpersonal context of emotion dynamics during PTSD treatment may enhance outcomes.

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.

Child maltreatment, parent-child relationship quality, and parental monitoring in relation to adolescent behavior problems: Disaggregating between and within person effects.

BACKGROUND: Parent-child relationship quality (PCRQ) and parental monitoring (PM) are associated with adolescent behavior problems following child maltreatment (CM). Whether these associations are best characterized as between (trait) or within-person (state) differences is unknown. OBJECTIVE: Disaggregate between and within-person effects for PCRQ and PM on adolescent behavior problems and test whether these effects vary as a function of prior CM. PARTICIPANTS AND SETTING: Participants (n = 941) are from the Longitudinal Studies on Child Abuse and Neglect (LONGSCAN). METHODS: Multi-level modeling was employed using PCRQ, PM, and adolescent behaviors assessed at ages 12, 14, and 16 and confirmed CM prior to age 12. RESULTS: At the between-person level, adolescents with higher average levels of PCRQ and PM had significantly lower initial levels of externalizing (b = -9.47 and -5.54, respectively, p's < 0.05; possible range 0-66) and internalizing behaviors (b = -4.45 and -6.41, respectively, p's < 0.001; possible range 0-62). At the within-person level, greater declines in externalizing and internalizing behaviors were found when individuals reported higher-than-usual levels of PCRQ (b = -4.99 and -2.59, respectively, for externalizing and internalizing, p's < 0.001) and PM (b = -3.58 and -1.69, respectively, for externalizing and internalizing, p's < 0.001). There was an interaction between PM and CM on internalizing behaviors over time (b = -1.15, p = 0.026). CONCLUSIONS: There are between and within-person effects of PCRQ and PM on adolescent behavior problems. Adolescents with CM histories and low levels of PM may be at risk for sustained internalizing behaviors.

The ILHBN: challenges, opportunities, and solutions from harmonizing data under heterogeneous study designs, target populations, and measurement protocols.

The ILHBN is funded by the National Institutes of Health to collaboratively study the interactive dynamics of behavior, health, and the environment using Intensive Longitudinal Data (ILD) to (a) understand and intervene on behavior and health and (b) develop new analytic methods to innovate behavioral theories and interventions. The heterogenous study designs, populations, and measurement protocols adopted by the seven studies within the ILHBN created practical challenges, but also unprecedented opportunities to capitalize on data harmonization to provide comparable views of data from different studies, enhance the quality and utility of expensive and hard-won ILD, and amplify scientific yield. The purpose of this article is to provide a brief report of the challenges, opportunities, and solutions from some of the ILHBN's cross-study data harmonization efforts. We review the process through which harmonization challenges and opportunities motivated the development of tools and collection of metadata within the ILHBN. A variety of strategies have been adopted within the ILHBN to facilitate harmonization of ecological momentary assessment, location, accelerometer, and participant engagement data while preserving theory-driven heterogeneity and data privacy considerations. Several tools have been developed by the ILHBN to resolve challenges in integrating ILD across multiple data streams and time scales both within and across studies. Harmonization of distinct longitudinal measures, measurement tools, and sampling rates across studies is challenging, but also opens up new opportunities to address cross-cutting scientific themes of interest.

Health behavior changes, such as prevention of suicidal thoughts and behaviors, smoking, drug use, and alcohol use; and the promotion of mental health, sleep, and physical activities, and decreases in sedentary behavior, are difficult to sustain. The ILHBN is a cooperative agreement network funded jointly by seven participating units within the National Institutes of Health to collaboratively study how factors that occur in individuals' everyday life and in their natural environment influence the success of positive health behavior changes. This article discusses how information collected using smartphones, wearables, and other devices can provide helpful active and passive reflections of the participants' extent of risk and resources at the moment for an extended period of time. However, successful engagement and retention of participants also require tailored adaptations of study designs, measurement tools, measurement intervals, study span, and device choices that create hurdles in integrating (harmonizing) data from multiple studies. We describe some of the challenges, opportunities, and solutions that emerged from harmonizing intensive longitudinal data under heterogeneous study and participant characteristics within the ILHBN, and share some tools and recommendations to facilitate future data harmonization efforts.

Valuing Negative Affect Weakens Affect-Health Linkages: Similarities and Differences Across Affect Valuation Measures.

Negative affect (NA) has been robustly linked to poorer psychological health, including greater depressive symptoms, personal burnout, and perceived stress. These associations, known as affect-health links, have been postulated by our research team to vary with different levels of negative affect valuation (NAV), such that people who evaluate NA states as more pleasant, helpful, appropriate, and/or meaningful may show weaker affect-health links. Another affect valuation construct is ideal NA, which is the degree to which people ideally want to experience NA states (i.e., desirability of affective states). The current study extends previous research by examining these two different measures of affect valuation (NAV and ideal NA) and comparing the extent to which they moderate affect-health links for psychological health and functioning. Participants from the Health and Daily Experiences (HEADE) study (N = 162 comprising of 56 younger adults and 106 older adults) completed questionnaires in a laboratory setting and ecological momentary assessments of NA 6 times a day for 7 consecutive days (i.e., trait NA). The results demonstrated that the two affect valuation constructs were distinct and showed different patterns of buffering effects. NAV attenuated the association between trait NA and depressive symptoms, personal burnout, and intolerance of uncertainty. Ideal NA attenuated affect-health links for depressive symptoms and perceived stress. These findings point to the importance of sharpening the distinctions between various affect valuation constructs to elucidate their unique contributions to attenuating affect-health links.

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.

Control Theory Forecasts of Optimal Training Dosage to Facilitate Children's Arithmetic Learning in a Digital Educational Application.

Education can be viewed as a control theory problem in which students seek ongoing exogenous input-either through traditional classroom teaching or other alternative training resources-to minimize the discrepancies between their actual and target (reference) performance levels. Using illustrative data from [Formula: see text] Dutch elementary school students as measured using the Math Garden, a web-based computer adaptive practice and monitoring system, we simulate and evaluate the outcomes of using off-line and finite memory linear quadratic controllers with constraintsto forecast students' optimal training durations. By integrating population standards with each student's own latent change information, we demonstrate that adoption of the control theory-guided, person- and time-specific training dosages could yield increased training benefits at reduced costs compared to students' actual observed training durations, and a fixed-duration training scheme. The control theory approach also outperforms a linear scheme that provides training recommendations based on observed scores under noisy and the presence of missing data. Design-related issues such as ways to determine the penalty cost of input administration and the size of the control horizon window are addressed through a series of illustrative and empirically (Math Garden) motivated simulations.

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.

A Square-Root Second-Order Extended Kalman Filtering Approach for Estimating Smoothly Time-Varying Parameters.

Researchers collecting intensive longitudinal data (ILD) are increasingly looking to model psychological processes, such as emotional dynamics, that organize and adapt across time in complex and meaningful ways. This is also the case for researchers looking to characterize the impact of an intervention on individual behavior. To be useful, statistical models must be capable of characterizing these processes as complex, time-dependent phenomenon, otherwise only a fraction of the system dynamics will be recovered. In this paper we introduce a Square-Root Second-Order Extended Kalman Filtering approach for estimating smoothly time-varying parameters. This approach is capable of handling dynamic factor models where the relations between variables underlying the processes of interest change in a manner that may be difficult to specify in advance. We examine the performance of our approach in a Monte Carlo simulation and show the proposed algorithm accurately recovers the unobserved states in the case of a bivariate dynamic factor model with time-varying dynamics and treatment effects. Furthermore, we illustrate the utility of our approach in characterizing the time-varying effect of a meditation intervention on day-to-day emotional experiences.

Relational impacts of capitalization in early parenthood.

Relationship difficulties are common during the transition to parenthood and may persist for years. Strategies that enhance couples' daily relational experiences early in the parenting years may serve a protective role. In general, engaging in a capitalization attempt (i.e., sharing personal good news) with one's romantic partner and perceiving the partner to be responsive are associated with better relationship outcomes among committed couples. However, it is unknown whether these relational benefits extend to the early parenting years or to other relational domains such as coparenting, which plays a central role in family functioning. The current study examined same-day associations between couples' capitalization process and relationship closeness and perceived coparenting support in a dyadic context during the first year of parenthood. A subsample of primarily non-Hispanic White coresident mixed-gender couples who participated in a randomized controlled trial of a transition to parenthood program (N = 141) completed daily diaries at 10 months postpartum for 8 consecutive days. On days when mothers shared, both partners reported greater closeness. On days when fathers shared, mothers reported greater closeness and perceived coparenting support. Furthermore, perceived partner responsiveness was associated with greater closeness for both partners and greater coparenting support for fathers. Fathers also perceived greater closeness and coparenting support on days when mothers shared about the child. Findings highlight the potential benefits of capitalization in early parenthood for both closeness and perceived coparenting support and suggest that capitalization may be a low cost, high yield strategy for enhancing new parents' daily relational experiences. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Effective Connectivity during an Avoidance-Based Pavlovian-to-Instrumental Transfer Task.

Pavlovian-to-instrumental transfer (PIT) refers to a phenomenon whereby a classically conditioned stimulus (CS) impacts the motivational salience of instrumental behavior. We examined behavioral response patterns and functional magnetic resonance imaging (fMRI) based effective connectivity during an avoidance-based PIT task. Eleven participants (8 females; Mage = 28.2, SD = 2.8, range = 25-32 years) completed the task. Effective connectivity between a priori brain regions engaged during the task was determined using hemodynamic response function group iterative multiple model estimation (HRF-GIMME). Participants exhibited behavior that was suggestive of specific PIT, a CS previously associated with a reinforcing outcome increased instrumental responding directed at the same outcome. We did not find evidence for general PIT; a CS did not significantly increase instrumental responding towards a different but related outcome. Using HRF-GIMME, we recovered effective connectivity maps among corticostriatal circuits engaged during the task. Group-level paths revealed directional effects from left putamen to right insula and from right putamen to right cingulate. Importantly, a direct effect of specific PIT stimuli on blood-oxygen-level-dependent (BOLD) activity in the left putamen was found. Results provide initial evidence of effective connectivity in key brain regions in an avoidance-based PIT task network. This study adds to the literature studying PIT effects in humans and employing GIMME models to understand how psychological phenomena are supported in the brain.

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.