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Based on findings from a simulation study, Parsons and McCormick (2024) argued that growth models with exactly two time points are poorly-suited to model individual differences in linear slopes in developmental studies. Their argument is based on an empirical investigation of the increase in precision to measure individual differences in linear slopes if studies are progressively extended by adding an extra measurement occasion after one unit of time (e.g., year) has passed. They concluded that two-time point models are inadequate to reliably model change at the individual level and that these models should focus on group-level effects. Here, we show that these limitations can be addressed by deconfounding the influence of study duration and the influence of adding an extra measurement occasion on precision to estimate individual differences in linear slopes. We use asymptotic results to gauge and compare precision of linear change models representing different study designs, and show that it is primarily the longer time span that increases precision, not the extra waves. Further, we show how the asymptotic results can be used to also consider irregularly spaced intervals as well as planned and unplanned missing data. In conclusion, we like to stress that true linear change can indeed be captured well with only two time points if careful study design planning is applied before running a study.

We collected biosignals from 63 participants and extracted the features corresponding to each level of exerted muscle force. Data were classified into typical and atypical patterns. Data analysis was performed using the Linear Latent Curve Model (LCM) and the Conditional Linear LCM. The typical patterns demonstrated a high degree of fit. Factors, such as ankle circumference and muscle mass, influenced the model intercept. A larger ankle circumference indicated attenuation of signal transmission from the tendon to the skin surface, leading to lower biosignal values. These results indicate that biosignals from the tendons near the ankle can be captured using piezoelectric film sensors. There are studies that define biosignals originating from tendons as mechanotendography. It has been demonstrated that the relationship between biosignals originating from tendons and the exerted muscle force can be explained linearly. Insights from this study may facilitate individualized approaches in the fields of motion control and rehabilitation. Physiological studies to elucidate the mechanisms underlying biosignal generation are necessary.

Research demonstrated a close relationship between loneliness and depressive symptoms, but it remains unclear whether these constructs reciprocally influence each other or whether the association is due to common causes. This study aimed at examining how loneliness and depressive symptoms jointly unfold across time and how the relationship varies both within and between individuals. We used survey data of N = 8472 older adults gathered in the English Longitudinal Study of Ageing, which included eight waves over a time period of up to 15 years. The relationship was analyzed using a latent curve model, allowing us to separate within-person processes from between-person differences in long-term growth. Results showed no prospective effects of loneliness on depressive symptoms (or vice versa) at the within-person level. Yet, within-person increases in loneliness were related to within-person increases in depressive symptoms at the same point in time. As regards the between-person effects, greater long-term growth in loneliness went along with greater long-term growth in depressive symptoms. Our findings did not support the assumption that loneliness and depressive symptoms influence each other over time, but rather suggest that the short- and long-term associations may be due to a common vulnerability to the same causes. Supplemental data for this article is available online at https://doi.org/10.1080/13607863.2022.2056138. [ABSTRACT FROM AUTHOR]

Although psychotherapy research has traditionally focused on decrease in distress, emotion research suggests the important role of positive emotional experience in healing and growing. Objective: The present study investigates the relationship between positive emotional experiences and working alliance. Method: We chose to investigate this relationship in accelerated experiential dynamic psychotherapy (AEDP), taking advantage of the modality's focus on both negative and positive emotional experiences. Fifty-eight clients receiving 16-sessions individual AEDP reported on their post-session levels of working alliance and positive emotions (enlivenment affect, positive relational affect, and peacefulness). The alliance-emotion relationship for each emotional categories was tested with separate disaggregated cross-lagged panel models. Results: Across the three categories, higher positive emotions at the end of the previous session were associated with higher working alliance at the end of the next session. On the other hand, working alliance did not contribute to any of the positive emotions in the next time point. Furthermore, the three emotion categories showed different patterns of development. Conclusion: The findings suggest that fostering positive emotions may be a promising venue to enhance working alliance. Furthermore, differentiating specific positive emotions is likely important both for research and practice. [ABSTRACT FROM AUTHOR]

Based on findings from a simulation study, Parsons and McCormick (2024) argued that growth models with exactly two time points are poorly-suited to model individual differences in linear slopes in developmental studies. Their argument is based on an empirical investigation of the increase in precision to measure individual differences in linear slopes if studies are progressively extended by adding an extra measurement occasion after one unit of time (e.g., year) has passed. They concluded that two-time point models are inadequate to reliably model change at the individual level and that these models should focus on group-level effects. Here, we show that these limitations can be addressed by deconfounding the influence of study duration and the influence of adding an extra measurement occasion on precision to estimate individual differences in linear slopes. We use asymptotic results to gauge and compare precision of linear change models representing different study designs, and show that it is primarily the longer time span that increases precision, not the extra waves. Further, we show how the asymptotic results can be used to also consider irregularly spaced intervals as well as planned and unplanned missing data. In conclusion, we like to stress that true linear change can indeed be captured well with only two time points if careful study design planning is applied before running a study. [ABSTRACT FROM AUTHOR]

BackgroundThis study aimed to investigate the influential factors of postoperative pain trajectories and morphine consumption after hepatic cancer surgery with a particular interest in multimodal analgesia.MethodsPatients receiving hepatic cancer surgery at a tertiary medical center were enrolled between 2011 and 2016. Postoperative pain scores and potentially influential factors like patient characteristics and the analgesic used were collected. Latent curve analysis was conducted to investigate predictors of postoperative pain trajectories and a linear regression model was used to explore factors associated with postoperative morphine consumption.Results450 patients were collected, the daily pain scores during the first postoperative week ranged from 2.0 to 3.0 on average. Male and higher body weight were associated with more morphine consumption (both P < 0.001) but reduced morphine demand was noted in the elderly (P < 0.001) and standing acetaminophen users (P = 0.003). Longer anesthesia time was associated with higher baseline pain levels (P < 0.001). In contrast, male gender (P < 0.001) and standing non-steroidal anti-inflammatory drugs (NSAIDs) use (P = 0.012) were associated with faster pain resolution over time.ConclusionsMultimodal analgesia with standing acetaminophen and NSAIDs had benefits of opioid-sparing and faster pain resolution, respectively, to patients receiving hepatic cancer surgery.

Background: Renal cell carcinoma (RCC) is the most common neoplasm in kidneys, and surgical resection remains the mainstay treatment. Few studies have investigated how the postoperative pain changes over time and what has affected its trajectory. This study aimed to characterize the variations in postoperative pain over time and investigate associated factors after RCC surgery. Methods: This retrospective study was conducted in a single medical center in Taiwan, where maximal pain scores in a numeric rating scale were recorded daily in the first five postoperative days (PODs) after RCC surgery. Latent curve models were developed, using two latent variables, intercept and slope, which represented the baseline pain and rate of pain resolution. These models explain the variations in postoperative pain scores over time. A predictive model for postoperative pain trajectories was also constructed. Results: There were 861 patients with 3850 pain observations included in the analysis. Latent curve analysis identified that female patients and those with advanced cancer (stage III and IV) tended to have increased baseline pain scores (p = 0.028 and 0.012, respectively). Furthermore, patients over 60 years, without PCA use (both p < 0.001), and with more surgical blood loss (p = 0.001) tended to have slower pain resolution. The final predictive model fit the collected data acceptably (RMSEA = 0.06, CFI = 0.95). Conclusion: Latent curve analysis identified influential factors of acute pain trajectories after RCC surgery. This study may also help elucidate the complex relationships between the variations in pain intensity over time and their determinants, and guide personalized pain management after surgery for RCC. [ABSTRACT FROM AUTHOR]

This study focused on feelings of lethargy and examined the realities of student apathy-like lethargy in general students from a longitudinal survey. A total of 121 university students who participated in the three surveys responded to questionnaires, including the low motivation scale, the lethargy scale, and the mental comfort scale. An analysis based on a conditional latent curve model was performed to examine the effect of the decline in academic motivation at the time of the first measurement on the intercept and slope. In the section, there was a negative effect on mental satisfaction/openness and comfort with others, but there was no effect on feelings of urgency/fatigue. In terms of inclination, neither satisfaction/openness nor sense of urgency/fatigue was affected by the decline in academic motivation. In other words, it was shown that a decrease in the motivation to study reduces the relaxation of the mind, but it is not accompanied by negative feelings. However, it was also clarified that the decrease in motivation did not affect the subsequent increase or decrease in mental space, and the characteristics of student apathy-like lethargy were found. In the future, support should be considered for the lethargy of university students based on the above characteristics. [ABSTRACT FROM AUTHOR]

Background: Although epidural analgesia (EA) provides reliable pain relief after major operations, few studies have explored how postoperative pain trajectories change over time in patients receiving EA and the associated factors. This study aimed to model the dynamic features of pain trajectories after surgery and investigate factors associated with their variations using latent curve analysis. Methods: This retrospective study was conducted at a single medical center in Taiwan, and data were obtained from patients receiving perioperative EA by electronic chart review. Mean numeric rating pain scores were recorded daily in the first five postoperative days. Patient demographics, surgical sites, and infusion pump settings were also collected. Latent curve models using two latent variables, intercept and slope, were developed to explain the variations in postoperative pain scores over time. The influences of potential predictors of postoperative pain trajectories were further evaluated for the final model determination. Results: Of the 1294 collected patients, the daily pain scores averaged 2.0 to 2.9 for different surgical sites. Among the nine significant factors influencing pain trajectories, chest and lower extremity surgery tended to induce less and more baseline pain, respectively, than those with abdomen surgery (both p < 0.001). In addition, male patients and those with a shorter anesthesia time had less baseline pain (p < 0.001 and p = 0.016, respectively). The older and lighter patients and those with chest surgery or American Society of Anesthesiologists class ≥ 3 tended to have milder decreasing trends in pain trajectories. A higher infusion rate was associated with an elevated baseline level and smoother decreasing trend in pain trajectory. The final model fit our data acceptably (root mean square error of approximation = 0.05, comparative fit index = 0.97). Conclusion: Latent curve analysis provided insights into the dynamic nature of variations in postoperative pain trajectories. Further studies investigating more factors associated with pain trajectories are warranted to elucidate the mechanisms behind the transitions of pain scores over time after surgery. [ABSTRACT FROM AUTHOR]

Background: Modelling trajectories of substance use over time is complex and requires judicious choices from a number of modelling approaches. In this study we examine the relative strengths and weakness of latent curve models (LCM), growth mixture modelling (GMM), and latent class growth analysis (LCGA).Design: Data were drawn from the Australian Temperament Project, a 36-year-old community-based longitudinal study that has followed a sample of young Australians from infancy to adulthood across 16 waves of follow-up since 1983. Models were fitted on past month alcohol use (n = 1468) and cannabis use (n = 549) across six waves of data collected from age 13-14 to 27-28 years.Findings: Of the three model types, GMMs were the best fit. However, these models were limited given the variance of numerous growth parameters had to be constrained to zero. Additionally, both the GMM and LCGA solutions had low entropy. The negative binomial LCMs provided a relatively well-fitting solution with fewer drawbacks in terms of growth parameter estimation and entropy issues. In all cases, model fit was enhanced when using a negative binomial distribution.Conclusions: Substance use researchers would benefit from adopting a complimentary framework by exploring both LCMs and mixture approaches, in light of the relative strengths and weaknesses as identified. Additionally, the distribution of data should inform modelling decisions. [ABSTRACT FROM AUTHOR]

Longitudinal data can provide inferences at both the between-person and within-person levels of analysis, but only to the extent that the statistical models chosen for data analysis are specified to adequately capture these distinct sources of association. The present work focuses on auto-regressive cross-lagged panel models, which have long been used to examine time-lagged reciprocal relations and mediation among multiple variables measured repeatedly over time. Unfortunately, many common implementations of these models fail to distinguish between-person associations among individual differences in the variables' amounts and changes over time, and thus confound between-person and within-person relations either partially or entirely, leading to inaccurate results. Furthermore, in the increasingly complex model variants that continue to be developed, what is not easily appreciated is how substantial differences in interpretation can be created by what appear to be trivial differences in model specification. In the present work, we aimed to (a) help analysts become better acquainted with the some of the more common model variants that fall under this larger umbrella, and (b) explicate what characteristics of one's data and research questions should be considered in selecting a model. Supplementary Materials include annotated model syntax and output using M plus , lavaan in R, and sem in Stata to help translate these concepts into practice. [ABSTRACT FROM AUTHOR]

Motor carriers' operational safety affects multiple stakeholders including truck drivers, motor carriers, insurance companies, shippers, and the general public. In this article, I devise and test theory regarding motor carriers' longitudinal performance for three classes of safety behaviors linked to carriers' accident rates-Unsafe Driving, Hours-of-Service Compliance, and Vehicle Maintenance-tracked by the Federal Motor Carrier Safety Administration as part of the Compliance, Safety, and Accountability (CSA) program. Specifically, I draw on core concepts from sociological agency theory and resource dependency theory to devise middle-range theory that generates never-before-tested hypotheses regarding carriers' longitudinal safety performance for these classes of safety behaviors after the start of the CSA program. The hypothesized predictions are tested by fitting a series of multivariate latent curve models to four years of panel data for a random sample of 484 large, for-hire motor carriers operating in the United States. The empirical findings corroborate the theoretical predictions and remain after robustness testing. These findings have important implications for scholars, motor carrier managers, procurers of motor carrier transportation services, and public policy makers. [ABSTRACT FROM AUTHOR]

Latent curve models have become a popular approach to the analysis of longitudinal data. At the individual level, the model expresses an individual’s response as a linear combination of what are called “basis functions” that are common to all members of a population and weights that may vary among individuals. This article uses differential calculus to define the basis functions of a latent curve model. This provides a meaningful interpretation of the unique and dynamic impact of each basis function on the individual-level response. Examples are provided to illustrate this sensitivity, as well as the sensitivity of the basis functions, to changes in the measure of time. [ABSTRACT FROM AUTHOR]

This article explores the impact of the recent recession on the well-being of the UK working age population by comparing two measures of well-being. One is a measure of evaluative subjective well-being, a measure which previous research has shown to be stable in the UK throughout the economic crisis. The second is a different but complementary measure of positive psychological health. By comparing the trajectories of these two measures using the same sample and modelling techniques the analysis examines how different measures may lead to different interpretations. Six waves of longitudinal data from Understanding Society and the British Household Panel Survey (BHPS) are used. Latent curve models are used to analyse change over time. The results corroborate previous research showing that people's evaluative subjective well-being remained relatively stable, on average, throughout the economic crisis. In contrast, the positive psychological health measure was found to decline significantly during the recession period. The paper highlights that what we measure matters. Using single measures as summaries of well-being masks the complexity of the term, and given their appeal in the social policy arena, single measures of well-being can be seen as problematic in some scenarios. [ABSTRACT FROM AUTHOR]

This study examined changes in the academic motivation of Japanese junior high school students through a two-year longitudinal survey, based on self-determination theory. Japanese students ( N = 410; 215 boys and 195 girls aged 12–13 years at the time of the first survey) completed the Japanese short-version of the Self-Regulation Questionnaire once each year during three consecutive grades (seventh, eighth, and ninth). The results of a latent curve model indicated that intrinsic and identified regulation (i.e., autonomous motivation) decreased and extrinsic and introjected regulation (i.e., controlled motivation) increased during junior high school. The results of ANOVA revealed the specific period during which academic motivation changed. In addition, a growth mixture model detected two characteristic profiles concerning motivational change: some students showed only decreases in autonomous motivation and others showed only increases in controlled motivation. Japanese junior high school students' motivation shifted from autonomous to controlled, but they did not become less motivated overall. [ABSTRACT FROM AUTHOR]

Popular longitudinal models allow for prediction of growth trajectories in alternative ways. In latent class growth models (LCGMs), person-level covariates predict membership in discrete latent classes that each holistically define an entire trajectory of change (e.g., a high-stable class vs. late-onset class vs. moderate-desisting class). In random coefficient growth models (RCGMs, also known as latent curve models), however, person-level covariates separately predict continuously distributed latent growth factors (e.g., an intercept vs. slope factor). This article first explains how complex and nonlinear interactions between predictors and time are recovered in different ways via LCGM versus RCGM specifications. Then a simulation comparison illustrates that, aside from some modest efficiency differences, such predictor relationships can be recovered approximately equally well by either model—regardless of which model generated the data. Our results also provide an empirical rationale for integrating findings about prediction of individual change across LCGMs and RCGMs in practice. [ABSTRACT FROM PUBLISHER]

This study examined developmental changes in self-assertion strategies used by toddlers in peer interactions. At a daycare center, 10 children in a one- and two-year-old class were observed during free play once each week for a year. The longitudinal trend of children's use of each strategy was analyzed by using a latent curve model. In addition, the developmental trajectory of each strategy was closely examined for each child. The results suggested that (1) assertion by vocalizing without words was observed more often at age 1 than at older ages, (2) assertive strategies accompanied by negative emotions (including aggression, crying and negative tone utterances) increased with age up to 24 months and then decreased, and (3) verbal strategies without negative tones, and more skillful strategies, increased with age. The importance of studying negative emotions that accompany self-assertion was discussed in relation to these findings. The discussion also focused on the merits of using latent curve models and examining the developmental trajectory of each individual child. [ABSTRACT FROM AUTHOR]

Abstract: The impurity of measures is considered as cause of erroneous interpretations of observed relationships. This paper concentrates on impurity with respect to the relationship between working memory and fluid intelligence. The means for the identification of impurity was the fixed-links model, which enabled the decomposition of variance into experimental and non-experimental parts. A substantial non-experimental part could be expected to signify impurity. In a sample of 345 participants error scores and reaction times, which were obtained by the Exchange Test, represented working memory, and Advanced Progressive Matrices served as measure of fluid intelligence. The four independent latent variables of the model associated with error scores and reaction times led to a multiple correlation .67 with the latent variable of fluid intelligence. However, there was impurity since the decomposition by means of the fixed-links model showed that only 45% of the common variance was due to working memory. [Copyright &y& Elsevier]

Latent curve models have become a useful approach to analyzing longitudinal data, due in part to their allowance of and emphasis on individual differences in features that describe change. Common applications of latent curve models in developmental studies rely on polynomial functions, such as linear or quadratic functions. Although useful for describing linear forms of change and some that are nonlinear, latent curve models based on polynomial functions are not suitable for describing many developmental processes that change in a nonlinear manner. This article considers nonlinear latent curve models that permit researchers to consider a variety of nonlinear functions to characterize developmental processes. An example is provided that considers simultaneous development of two behaviors. [ABSTRACT FROM AUTHOR]