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Purpose The present study aimed to examine (a) how fatigue severity changes during the course of chemotherapy in patients with hematologic cancer and (b) whether cytokines (IL-1 alpha, IL-1 beta, IL-6) are associated with fatigue change after controlling for demographic and clinical factors (e.g., hemoglobin/hematocrit, medications, comorbid conditions). Methods This observational cohort study used data from 148 hematological cancer patients four times: prior to chemotherapy, on the last day of chemotherapy, 1 week after the chemotherapy completion, and 1 month after baseline assessment. Latent growth curve modeling was used to examine the longitudinal association of fatigue severity with cytokines and hemoglobin. Results A quadratic growth curve model fit the data well, indicating model tenability, and explained a large amount of variance in fatigue across measurement time points. Fatigue slightly increased toward the end of chemotherapy and decreased with time after chemotherapy completion. The influence of IL-6 on fatigue was significant at all time points except at the last assessment occasion (i.e., 1 month after the baseline assessment). The influence of IL-6 on fatigue was independent (unique) from the impact of hemoglobin level. Age and chemotherapy given for the first line of treatment significantly influenced the rate of fatigue change over time. Age also influenced the change pattern’s shape. Conclusions Fatigue severity changes across the course of chemotherapy within the context of IL-6 activity, not the hemoglobin level. The influence of IL-6 may be limited during and shortly after chemotherapy. These findings inform the development of new symptom management strategies.

Leisure-time physical activity (LTPA) is an important means of enhancing well-being. Although previous research has typically documented the cross-sectional associations between LTPA and well-being, the longitudinal bidirectional association remains relatively unexplored. Using a latent growth curve model, this study examined the longitudinal association between the intensity of LTPA, psychological well-being, and social well-being. The results revealed that the longitudinal associations differed, depending on the intensity of the LTPA and the type of well-being. Specifically, the longitudinal associations of moderate LTPA with psychological and social well-being were bidirectional. However, psychological and social well-being at baseline directly influenced the growth of vigorous LTPA, but not conversely (i.e. vigorous LTPA at baseline → change in psychological and social well-being), indicating no bidirectional association. These findings could contribute to a better understanding of ways in which different intensities of LTPA are associated with distinct types of well-being over a long time.

Software-as-a-Service (SaaS) Cloud computing as the next stage of internet evolution provides all the computing resources as services over the Internet. In the SaaS cloud computing research area, there are many studies from the user’s point of view, but there is relatively little research on the supplier’s success strategy. The purpose of this study is to empirically analyze the factors that determine the market competitiveness necessary for SaaS cloud computing providers to survive in the mid to long term. We presented application dimension and technology maturity as SaaS idiosyncratic factors and show how these factors influence the business performance of SaaS providers through a differentiation strategy and low-cost strategy. Using a multivariate latent growth curve model, this study analyzed 199 strategic business units of SaaS cloud computing providers in Korea for three years. Our results find that SaaS cloud computing idiosyncratic factors did not significantly enhance the software providers’ business performance in the early stage. However, they significantly affected the growth rate of their customer base and financial performance as the SaaS technology became mature over time. In addition, this study identifies a set of business, strategic, and technical considerations to guide the practitioners’ decision-making process for selecting an appropriate SaaS cloud computing model.

This study examined the associations between childhood experiences and frailty trajectory among middle-aged and older Chinese adults. Data were derived from the China Health and Retirement Longitudinal Study. We used data from all four waves (i.e., 2011, 2013, 2015, 2018) and the life history survey in 2014. Data for 10,963 respondents were included. Latent growth curve models were conducted to examine the proposed model. The results show that adverse childhood experiences, self-rated childhood socioeconomic status, and the objective indicators of childhood health and health care were associated with both the baseline level and change rate of frailty. The educational attainment of fathers and perceived childhood health and healthcare conditions were associated with baseline frailty only. Our findings highlight the crucial role of childhood antecedents in the progression of frailty in later life. We further found strong evidence that childhood is an essential life stage for human development. Future social policies and interventions should use childhood experiences as a screening tool and promote child protection, health education, and life course interventions.

The Great Recession derived from USA subprime crisis involved the European countries in two different steps: in the first phase, referred to 2008–2009, the finance contraction and the bank failures spread out across the whole Atlantic area involving, above all, the financial market but also influencing the real economy. The second phase, started in 2011 and lasted until 2014–2015, affected the Euro zone and sovereign debts of the countries until 2015 in depth. Also for Italy, the crisis period was characterized by a deep negative conjuncture until 2009, by a slight recovery until the first half of 2011 and by an intense recession from 2011 to 2015. The aim of this work is to collect evidence on the Italian manufacturing system with the following goals: to investigate the riskiness–distress risk trend across industries in 2008–2017 period and to understand how this trend inluences the performance of the company at the end of the period. To perform this analysis a Latent Growth Curve Model is proposed, using an important Italian private database containing the book-value data of the joint-stock company Italian firms.

Cohort effect refers to the social phenomenon that a certain event manifests differently depending on a group that is born in the same year or a similar time period. It is important to understand adolescents’ depressive symptoms vary from generation to generation. We studied the changes of the depressive symptoms during the eighth to tenth grade and examined the difference between the 1997-birth cohort and 2000-birth cohort. The study included 2,070 students (2014–2016 of 2000-birth-cohort data, from 8th to 10th grade, mean age = 12.95–14.95 years) and 2,278 students (2011–2013 of 1997-birth-cohort data, from 8th to 10th grade, mean age = 12.90–14.90 years) who participated in the Korean Children and Youth Panel Survey. Latent growth curve modeling and multi-group analysis were used to compare depressive symptom trajectories between cohorts. The result of comparing the depression levels of the cohorts showed the initial values and change rates verifying the presence of a significant cohort effect—the depression level of the 2000-birth-cohort (initial value = 17.75) was significantly lower than the depression level of the 1997-birth-cohort (initial value = 19.49). This study has significance in that adolescents’ depression has both age and cohort effects. It is speculated that the cohort effect may have been impacted by two major events: The Children Problem-Behavior Screening Questionare Test (mental health screening test), implemented nationwide in 2012, and the Sewol Ferry Disaster, which occurred in 2014. Based on the results, suggestions for future research were discussed.

To date, little is known about the short-term dynamics of perfectionism in undergraduate students, especially in certain potentially activating contexts, such as the proximity of the exam session. The aim of the present study was to address this methodological limitation and literature gap by conducting a latent growth curve model analysis of students’ perfectionistic concerns and strivings in the proximity of exams, to detect and explain the potential heterogeneity in their growth trajectories. The study sample consisted of 242 undergraduate students who participated at four-time points (228 at T0, 171 at T1, 225 at T2, and 146 at T3), spaced four weeks apart, between the beginning of the first academic semester and the beginning of the exam session. The results showed that while perfectionistic concerns displayed a significant overall growth trajectory during this period of time, perfectionistic strivings were rather stable personality traits, less dependent on contextual factors. Regarding the individual differences in perfectionism dimensions, the findings showed that there was a significant inter-individual variability around both the initial level and the overall growth trajectory of perfectionistic concerns. However, only the perceived teachers' expectations explained significantly part of the variations around the initial level of perfectionistic concerns.

Over the past two decades, the number of international cooperative patents has grown significantly with the development of globalization. Although previous studies have almost exclusively used econometric methods, we argue that international cooperative patents change over time and that the relevant actors are dynamic, connected, and coexist to determine a country’s position in the international network of such patents. This study verified that different dimensions in the growth trajectory of the central patent international cooperative network curve form covariance structure perspectives using a latent growth curve model. Additionally, past studies have rarely examined whether technological concentration affects national technology innovation capacities; here, we integrated technological concentration with the estimation model. The results indicated that knowledge stock has a positive effect on the evolutionary growth rate of international cooperative patent network centrality. Moreover, technological concentration was found to strengthen the effect of knowledge stock on network centrality. An experimental map was produced to illustrate the interrelationships of the dimensions, which may be used as a reference by the government.

We aimed to examine the time evolution of gross motor function and activities of daily living (ADL) performance in children and adolescents with cerebral palsy (CP). Data on 222 children and adolescents with CP were collected over a 3-year period. Gross motor function and ADL performance were assessed using the gross motor function measure and the functional skill section of the Pediatric Evaluation of Disability Inventory, respectively. Latent growth curve models of zero-change, linear, 2nd-year, and 3rd-year variation in gross motor function and ADL performance were constructed. The relation between these two indicators of movement disability was evaluated via multivariate latent growth modeling. The 2nd- and 3rd-year variation models reflected the time evolution of gross motor function and ADL performance, respectively. The initial level of gross motor function affected the initial level and growth rate of ADL performance. Additionally, the rate of change in gross motor function affected the rate of the change in ADL performance. Children and adolescents with CP should undergo continuous rehabilitation focused on gross motor function.

Social lag is an indicator that measures social development in Mexico. The institutions in charge of measuring poverty require studies for measuring the extent to which social programs are efficient to combat poverty. In the past, SL has been measured using principal component analysis but this approach cannot be used for longitudinal studies. We present an alternative analysis for measuring social lag to overcome this difficulty. For this study, we use the census data for the years 2000, 2005, 2010, and 2015, which include 2446 municipalities. The results are summarized in three steps: (1) The number of SL indicators is reduced from 11 to 6 using confirmatory factor analysis techniques and the adjustment is found to be satisfactory; (2) mixture latent growth curve models were used to estimate growth trajectories of the municipalities from 2000 to 2015; (3) we used LISA Maps and the Moran index to identify regions of potential growth. In conclusion, we observed an unequal development of the municipalities in Mexico during 15 years of application of the social policy to reduce poverty and inequality. The present work contributes to providing evidence for elaborating public policies for targeting communities in need.