Longitudinal Dataset Research Articles

The secret of voice: How acoustic characteristics affect video creators' performance on Bilibili

The importance of voice has been well acknowledged in sensory decision-making. Yet, past literature on video creators' performance did not shed much light on the impact of video creators' acoustic characteristics. Building on signaling theory of portfolios, we examine how the acoustic characteristics of a video creator and the signals of video quantity affect the number of likes a video creator receives and the change in the number of the creator's followers. Using a longitudinal dataset obtained from Bilibili through automated speech recognition analytics and text analysis approach, econometrics models are employed to test the research model. Findings indicate that loudness variability of a video creator significantly increases the number of likes the creator receives, and exerts a positive effect on the change in the number of the creator's followers. In contrast, vocal pitch has a significant positive impact on the number of likes but a significant negative impact on the change in the number of followers. Results further suggest that a video creator's performance varies according to the signals of video quantity, i.e., the number of weekly published videos and the average length of videos. Our results could offer theoretical contributions and practical insights for video creators to enhance their performance by adjusting the acoustic characteristics and the signals of video quantity wisely.

Changes in adult well-being and economic inequalities: An exploratory observational longitudinal study (2002–2010) of micro-level trends among Tsimane’, a small-scale rural society of Indigenous People in the Bolivian Amazon

Knowing what happens over time to the lifeways of people in contemporary small-scale non-industrial societies of the rural Global South matters because it helps assess changes in the quality of life of underrepresented groups. It has been hard to answer the question because longitudinal information is rarely collected in such settings. A longitudinal dataset of nine years (2002–2010) from a horticultural-foraging society of Indigenous People in the Bolivian Amazon (Tsimane’) is used for an exploratory analysis of micro-level trends in indicators of well-being and economic inequalities. We selected 13 Tsimane’ villages (from ∼ 100) that varied in proximity to town and surveyed all households in each village. ∼ 240 households were followed yearly to estimate trends of 21 outcomes (e.g., income, sociality, macronutrients). For each economic outcome, annual and all-years-combined Gini coefficients were estimated for the entire sample across the 13 villages. We show a rise in total asset wealth, a change in asset composition (less traditional wealth, more commercial wealth), higher monetary value of foods eaten, and better-perceived health, but a decline in caloric and protein consumption and no marked gender differences in objective or hedonic measures of well-being. Economic inequalities were non-trivial and showed no marked trend but varied between years; asset inequality varied less than income inequality. We document the value of longitudinal, locally grounded indexes of well-being to obtain a granular view of micro-level changes in well-being and the possible use of inequality in the consumption of calories and macronutrients as a valid proxy for income inequality in rural areas of the Global South with tenuous links to the market economy.

Identifying opportunities for prevention of adverse outcomes following female genital fistula repair: protocol for a mixed-methods study in Uganda

BackgroundFemale genital fistula is a traumatic debilitating injury, frequently caused by prolonged obstructed labor, affecting between 500,000-2 million women in lower-resource settings. Vesicovaginal fistula causes urinary incontinence, and other morbidity may occur during fistula development. Women with fistula are stigmatized, limit social and economic engagement, and experience psychiatric morbidity. Improved surgical access has reduced fistula consequences yet post-repair risks impacting quality of life and well-being include fistula repair breakdown or recurrence and ongoing or changing urine leakage or incontinence. Limited evidence on risk factors contributing to adverse outcomes hinders interventions to mitigate adverse events. This study aims to quantify these adverse risks and inform clinical and counseling interventions to optimize women’s health and quality of life following fistula repair through: identifying predictors and characteristics of post-repair fistula breakdown and recurrence (Objective 1) and post-repair incontinence (Objective 2), and to identify feasible and acceptable intervention strategies (Objective 3).MethodsThis mixed-methods study incorporates a prospective cohort of women with successful vesicovaginal fistula repair at approximately 12 fistula repair centers in Uganda (Objectives 1-2) followed by qualitative inquiry among key stakeholders (Objective 3). Cohort participants will have a baseline visit at the time of surgery followed by data collection at 2 weeks, 6 weeks, 3 months and quarterly thereafter for 3 years. Primary predictors to be evaluated include patient-related factors, fistula-related factors, fistula repair-related factors, and post-repair behaviors and exposures, collected via structured questionnaire at all data collection points. Clinical exams will be conducted at baseline, 2 weeks post-surgery, and for outcome confirmation at symptom development. Primary outcomes are fistula repair breakdown or fistula recurrence and post-repair incontinence. In-depth interviews will be conducted with cohort participants (n ~ 40) and other key stakeholders (~ 40 including family, peers, community members and clinical/social service providers) to inform feasibility and acceptability of recommendations.DiscussionParticipant recruitment is underway. This study is expected to identify key predictors that can directly improve fistula repair and post-repair programs and women’s outcomes, optimizing health and quality of life. Furthermore, our study will create a comprehensive longitudinal dataset capable of supporting broad inquiry into post-fistula repair health.Trial Registration ClinicalTrials.gov Identifier: NCT05437939.

Patient-Centered Positive Factors Influencing Glycemic Control in Adolescents with Type 1 Diabetes Mellitus in China: A Cross-Sectional and Longitudinal Study.

This study aimed to explore the stable longitudinalpatient-centered self-protectivefactors of glycosylated hemoglobin (HbA1c) in adolescents with type 1 diabetesmellitus(T1DM). We used both cross-sectional and longitudinal datasets at the Diabetes Education Center and National Endocrine and Metabolism Centre of a university hospital in China from April 2020 to July 2022. Participants were assessed using the Adolescent Diabetic Behavior Rating Scale (DBRS), Diabetes Strengths and Resilience Measure for Adolescents (DSTAR-Teen). HbA1c and other clinical variables were obtained from the medical record at the same time. 266 adolescents (131 male, age 14.1±3.9 years) completed the cross-sectional assessments and 131 (62 male, age 14.6±3.3 years) participated in a follow-up at a 1-year visit interval. Logistic regression analysis of cross-sectional data of 266 cases showed that there were significant positive effects between pump treatment (β=0.090, OR 2.460, P=0.005), DBRS scores (β=2.593, OR 13.366, P=0.002) and the meeting of standard HbA1c (<7.5%, 58 mmol/mol). Disease duration (β=-0.071, OR 0.932, P=0.033) was negatively correlated with it. The longitudinal multivariate generalized estimation equation model showed that DBRS scores (β=3.165, OR 23.681, P=0.009) and DSTAR-Teen scores (β=0.050, OR 1.051, P=0.012) had a positive influence on the meeting of standard HbA1c over one year time of 131 cases. Self-care and resilience had higher cross-temporal stability in influencing glycemic control over time. To reach a better glycemic control and improve long-term health outcomes, attention should be paid to the detection and enhancement of these patient-centered promoters.

Health parameters for wild Carnaby's cockatoo (Zanda latirostris) nestlings in Western Australia: results of a long-term study

ABSTRACT The collection of baseline health data is an essential component of an endangered species conservation program. As for many wildlife species, there are minimal health data available for wild populations of the endangered Carnaby’s cockatoo (Zanda latirostris). In this study, 426 wild Carnaby’s cockatoo nestlings were sampled from nine breeding sites throughout the range of the species over an 11-year period. In addition to a physical examination, samples were collected to test for hematologic and biochemical parameters, psittacine beak and feather disease virus (BFDV), avian polyomavirus (APV), psittacine adenovirus, psittacine herpesvirus, Chlamydia, disease serology and endoparasites. Environmental sampling was performed to screen for BFDV and APV in nest hollows. Descriptive health data are presented for nestlings of this species, with BFDV, APV and Chlamydia infections reported. Reference intervals for hematologic and biochemical parameters are presented in three age groups, and factors affecting blood analytes and body condition index are discussed. This longitudinal dataset provides insights into health parameters for Carnaby’s cockatoo nestlings and a reference for future monitoring of breeding populations.

Influences of public health emergency and social isolation on older adults' wellbeing: evidence from a longitudinal study.

Previous research has identified social isolation as a significant detriment to the wellbeing of older adults. However, studies that consider endogenous issues are scarce. The present paper examines the impact of the recent exogenous shock, the COVID-19 pandemic on the wellbeing of the older adult population using a longitudinal dataset from China for the period 2016-2020. The results of this study indicate that the life satisfaction of Chinese older adults was negatively affected, e particularly in regions where social distancing measures were more strictly enforced. Declines in physical and mental health were found to be attributable to declines in life satisfaction. Those who experienced greater exposure to the pandemic were more likely to suffer from chronic disease, illness, and insomnia, and many found it challenging to complete tasks during the lockdown. Furthermore, heterogeneity estimation shows that these effects are stronger among the rural older adult, females, those without a spouse, and those with less education.

Cortical structure reorganization and correlation with attention deficit in subcortical stroke: An underlying pattern analysis

BackgroundSubcortical stroke may significantly alter the cerebral cortical structure and affect attention function, but the details of this process remain unclear. The study aimed to investigate the neural substrates underlying attention impairment in patients with subcortical stroke. Materials and MethodsIn this prospective observational study, two distinct datasets were acquired to identify imaging biomarkers underlying attention deficit. The first dataset consisted of 86 patients with subcortical stroke, providing a cross-sectional perspective, whereas the second comprised 108 patients with stroke, offering longitudinal insights. All statistical analyses were subjected to false discovery rate correction upon P < 0.05. ResultsIn the chronic-stage data, the stroke group exhibited significantly poorer attention function compared with that of the control group. The cortical structure analysis showed that patients with stroke exhibited decreased cortical thickness of the precentral gyrus and surface area of the cuneus, along with an increase in various frontal, occipital, and parietal cortices regions. The declined attention function positively correlated with the superior frontal gyrus cortical thickness and supramarginal gyrus surface area. In the longitudinal dataset, patients with stroke showed gradually increasing cortical thickness and surface area within regions of obvious structural reorganization. Furthermore, deficient attention positively correlated with supramarginal gyrus surface area both at the subacute and chronic stages post-stroke. ConclusionsSubcortical stroke can elicit dynamic reorganization of cortical areas associated with attention impairment. Moreover, the altered surface area of the supramarginal gyrus is a potential neuroimaging biomarker for attention deficits.

Does the future of Industry 4.0 lie in betweenness centrality? A structural network analysis of The Republic of Korea’s patent citation maps for the green industry

Do emerging industrial nations have little hope in the face of the new technological revolution 4.0, given the powerful self-reinforcing mechanisms characteristic of the innovation systems in central regions? Or can socio-technical trajectories, initially emerging as niches but later evolving into sectors or even networks, pave the way for technological leapfrogging in these emerging countries? If so, can we envision specific techno-industrial policies to facilitate these leaps, and more importantly, can we develop specific tools and methods to diagnose and promote such policies?This paper aims to contribute to the improvement and application of a Structural Network Analysis (SNA) methodology to identify, within a large set of longitudinal patent data, in a subdomain of industry 4.0 (namely, green technologies), the most promising technological domains for a given country under study (South Korea).The interest of this perspective on technological economic policies is to target a technological domain that is both critical and stimulating enough to generate a network effect, yet sufficiently niche to not require capital beyond the capacity of an emerging nation.In the current state of our intermediate research results, the findings are only partially satisfactory. The results regarding the most promising technologies appear significant only half of the time (whereas in the reference study, they tend towards 100%). Nevertheless, many avenues for improvement in research remain open.JEL classification: 030.

Kenya Psychosis-Risk Outcomes Study (KePROS): Development of an Accelerated Medicine Partnership Schizophrenia-Aligned Project in Africa.

The Accelerating Medicines Partnership Schizophrenia (AMP SCZ) funds a longitudinal study of 43 research sites across 5 continents to develop tools to stratify developmental trajectories of youth at clinical high risk for psychosis (CHR) and identify homogenous targets for future clinical trials. However, there are no sites in Africa, leaving a critical gap in our knowledge of clinical and biological outcomes among CHR individuals. We describe the development of the Kenya Psychosis-Risk Outcomes Study (KePROS), a 5-year NIH-funded project in Kenya designed to harmonize with AMP SCZ. The study will recruit over 100 CHR and 50 healthy participants and conduct multiple clinical and biomarker assessments over 2 years. Capacity building is a key component of the study, including the construction of an electroencephalography (EEG) laboratory and the upgrading of a local 3 T magnetic resonance imaging (MRI) machine. We detail community recruitment, study methodologies and protocols, and unique challenges with this pioneering research in Africa. This paper is descriptive only. Planned future analyses will investigate possible predictors of clinical outcomes and will be compared to results from other global populations. KePROS will provide the research community with a rich longitudinal clinical and biomarker dataset from an African country in the developing Global South, which can be used alongside AMP SCZ data to delineate CHR outcome groups for future treatment development. Training in mental health assessment and investment in cutting-edge biomarker assessment and other technologies is needed to facilitate the inclusion of African countries in large-scale research consortia.

Ophthalmic evaluation of a population simulated with AI: Epidemiology and future disease trends

The development of AI models for the diagnosis of diseases in Ophthalmology is accelerating rapidly. These models, however, mostly refer to a single or only a few diseases. In addition, only a few examination techniques are considered in a given model. For example, many current AI models only consider fundus images for their diagnosis. Thus, general clinical information that is also available about a patient is often neglected. This information includes, for example, age, sex and secondary diagnoses.In most cases, diseases have different frequencies depending on age and sex. This leads to a different a priori probability for a disease diagnosis depending on the patient's age. When considering several diseases in one model, this class imbalance must be considered in the model. Large longitudinal data sets of disease histories of individual persons are rare or under high data privacy requirements.With the aim of more holistic AI models that include multiple diagnoses, a novel AI model was developed that is able to simulate longitudinal disease histories of individual (synthetic) patients. This was made possible based on data from the German Federal Statistical Office and a newly developed AI algorithm. In this present study, the model is used to simulate different diagnoses of the German population that lead to inpatient hospital admissions. The data were generated as completely synthetic data and have no data privacy requirements. Information on epidemiology and disease incidence can be derived from this model. Furthermore, it is possible to generate forecasts about future hospital admissions and their underlying diagnoses by extrapolating the model into the future.

The Art of Adversity Thinking: Political Turnover and Firm Innovation

By integrating the political strategy literature and research on innovation, this article develops a theoretical framework and empirically tests how and why political turnover affects firm innovation. Drawing on the attention-based view, we argue that firms also use innovation as an attention-catching strategy to seek opportunities in uncertain environments. Furthermore, we highlight how the uncertainty rooted in political turnover varies by identifying two contingent values: 1) politicians&#x2019; career concerns and 2) firms&#x2019; political hierarchy. Using a longitudinal data set of industrial enterprises in the context of China, we find that there is a significant positive relationship between the replacement of government leaders at prefectural city level and firm innovation. Moreover, this positive relationship is strengthened when new government leaders are promoted locally, but is less salient if the politicians are at an early stage of their career or firms are affiliated with higher hierarchies. Our research expands the literature on uncertainty by highlighting the role of political agents, contributes to the theory of the attention-based view by treating attention from key external stakeholders as a key resource, and advances the understanding of how firms strategically respond to political shocks in China.

Monitoring the impact of COVID-19 on the travel behavior of train travelers in the Netherlands

Mobility patterns and transport systems have been heavily impacted due to the COVID-19 pandemic. Public transport is impacted heavily, as governments worldwide advised against using it. This paper presents the data collection effort initiated by NS (Dutch Railways) and Delft University of Technology to capture changes in travel behavior, attitudes and intentions related to the COVID-19 pandemic among Dutch train travelers. The survey set-up, data collection process, data validation and potential of the dataset are discussed. The data collection effort proves to be a valuable longitudinal data set that is ground for many research opportunities and policy insights.

Prognosis and conditional survival among women with newly diagnosed ovarian cancer

ObjectiveAn important question in determining long-term prognosis for women with ovarian cancer is whether risk of death changes the longer a woman lives. Large real-world datasets permit assessment of conditional survival (CS) given both prior overall survival (OS) and real-world progression-free survival (rwPFS). MethodsUsing a longitudinal dataset from US oncology centers, this study included 6778 women with ovarian cancer. We calculated CS rates as the Kaplan-Meier probability of surviving an additional 1 or 5 years, given no mortality (OS) or disease progression (rwPFS) event in the previous 0.5–5 years since first-line chemotherapy initiation, adjusted for factors associated with OS based on multivariable Cox regression. ResultsMedian study follow-up was 9 years (range, 1–44) from first-line initiation to data cutoff (17-Feb-2021). Median OS was 58.0 months (95% CI, 54.9–60.8); median rwPFS was 18.4 months (17.4–19.4). The adjusted 1-year CS rate (ie, rate of 1 year additional survival) did not vary based on time alive, whereas the adjusted 5-year CS rate increased from 48.5% (47.0%–50.1%) for women who had already survived 6 months to 66.4% (63.3%–69.6%) for those already surviving 5 years (thus surviving 10 years total). The adjusted 1-year CS rate increased from 90.4% (89.5%–91.4%) with no rwPFS event at 6 months to 97.6% (96.4%–98.8%) with no rwPFS event at 5 years; adjusted 5-year CS rate increased from 53.7% (52.0%–55.5%) to 85.0% (81.2%–88.9%), respectively. ConclusionsThis analysis extends the concept of CS by also conditioning on time progression-free. Patients with longer rwPFS experience longer survival than patients with shorter rwPFS.

3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs.

Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models. Contrastive methods are a prominent family of SSL that extract similar representations of two augmented views of an image while pushing away others in the representation space as negatives. However, the state-of-the-art contrastive methods require large batch sizes and augmentations designed for natural images that are impractical for 3D medical images. To address these limitations, we propose a new longitudinal SSL method, 3DTINC, based on non-contrastive learning. It is designed to learn perturbation-invariant features for 3D optical coherence tomography (OCT) volumes, using augmentations specifically designed for OCT. We introduce a new non-contrastive similarity loss term that learns temporal information implicitly from intra-patient scans acquired at different times. Our experiments show that this temporal information is crucial for predicting progression of retinal diseases, such as age-related macular degeneration (AMD). After pretraining with 3DTINC, we evaluated the learned representations and the prognostic models on two large-scale longitudinal datasets of retinal OCTs where we predict the conversion to wet-AMD within a six-month interval. Our results demonstrate that each component of our contributions is crucial for learning meaningful representations useful in predicting disease progression from longitudinal volumetric scans.

Evaluating the Predictive Value of Glioma Growth Models for Low-Grade Glioma After Tumor Resection.

Tumor growth models have the potential to model and predict the spatiotemporal evolution of glioma in individual patients. Infiltration of glioma cells is known to be faster along the white matter tracts, and therefore structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) can be used to inform the model. However, applying and evaluating growth models in real patient data is challenging. In this work, we propose to formulate the problem of tumor growth as a ranking problem, as opposed to a segmentation problem, and use the average precision (AP) as a performance metric. This enables an evaluation of the spatial pattern that does not require a volume cut-off value. Using the AP metric, we evaluate diffusion-proliferation models informed by structural MRI and DTI, after tumor resection. We applied the models to a unique longitudinal dataset of 14 patients with low-grade glioma (LGG), who received no treatment after surgical resection, to predict the recurrent tumor shape after tumor resection. The diffusion models informed by structural MRI and DTI showed a small but significant increase in predictive performance with respect to homogeneous isotropic diffusion, and the DTI-informed model reached the best predictive performance. We conclude there is a significant improvement in the prediction of the recurrent tumor shape when using a DTI-informed anisotropic diffusion model with respect to istropic diffusion, and that the AP is a suitable metric to evaluate these models. All code and data used in this publication are made publicly available.

Are central eating disorder network symptoms sensitive to item selection and sample? Implications for conceptualization of eating disorder psychopathology from a network perspective.

Item selection is a critical decision in modeling psychological networks. The current preregistered two-study research used random selections of 1,000 symptom networks to examine which eating disorder (ED) and co-occurring symptoms are most central in longitudinal networks among individuals with EDs (N = 71, total observations = 6,060) and tested whether centrality changed based on which items were included in the network. Participants completed 2 weeks of ecological momentary assessment (five surveys/day). In Study 1, we obtained initial strength centrality values by estimating an a priori network using eight items with the highest means. We then estimated 1,000 networks and their centrality from a random selection of unique eight-item symptom combinations. We compared the strength centrality from the a priori network to the distribution of strength centrality estimates from the random-item networks. In Study 2, we repeated this procedure in an independent longitudinal dataset (N = 41, total observations = 4,575) to determine if our results generalized across samples. Shame, guilt, worry, and fear of losing control were consistently central across networks, regardless of items included in the network or sample. Results suggest that these symptoms may be important to the structure of ED psychopathology and have implications for how we understand the structure of ED psychopathology. Existing methods for item inclusion in psychological networks may distort the structure of ED symptom networks by either under- or overestimating strength centrality, or by omitting consistently central symptoms that are nontraditional ED symptoms. Future research should consider including these symptoms in models of ED psychopathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Cold Storage Order Picking Performance: Effects of Load Unit Utilization and Product Volume

Increasing labor costs in retail have intensified the focus on order-fulfillment performance, particularly in warehouse operations. In this context, manual picker-to-parts systems, especially in cold storage environments processing deep-frozen products below -20°C, are of significant interest. Our study explores order picking in such cold storage environments, where products are packed into insulated, roll containers serving as load units. While existing research has explored the effects of product characteristics such as weight, volume, and different load units like pallets from a theoretical perspective, the specific interplay between load unit utilization levels and product volume remains underexplored in practical settings. To address this gap, we conducted a field study focusing on a cold storage of a prominent German brick-and-mortar grocery retailer and analyzed a comprehensive longitudinal dataset comprising 227,274 storage location visits by 29 order pickers in January 2023. Employing a mixed-effects model, we examined the impact of these factors on order picking time (OPT). Our findings revealed a subtle relationship: a 1% increase in load unit utilization level correlates with a 1% increase in OPT, signifying that higher utilization levels negatively impact pickers’ performance. Notably, our analysis also indicated that product volume moderates this effect. Specifically, lower product volumes can mitigate the adverse effects of high load unit utilization, whereas higher product volumes amplify these effects. This research offers valuable insights for warehouse managers, particularly in cold storage settings on finding the balance between OPT and load unit utilization.

The impact of COVID-19 on household income and participation in the agri-food value chain: Evidence from Ethiopia

The effects of COVID-19 have been highly heterogeneous, crucially depending on household livelihoods. In the context of households reliant on agri-food systems, the extent of these effects significantly depends on their position within the value chain. An assessment of the COVID-19 effects along the agri-food value chain and the identification of pivotal factors influencing these outcomes are key for designing appropriate responses and targeting the population most in need should a crisis akin to COVID-19 emerge in the future. Using a longitudinal dataset from Ethiopia, composed of a pre-COVID baseline and six follow-up phone-based surveys, this paper estimates the COVID-19-induced change in household income and job participation, tracing its evolution throughout seven months after the pandemic onset. Applying both longitudinal and cross-sectional econometric models, we show that the COVID-19 shock reduced both employment and income, with increasingly negative impacts over time. Despite initial resilience in the face of restrictive measures, farming eventually emerged as the most affected segment within the agri-food value chain over the medium term. Access to formal institutions such as insurance and credit services, formal contractual arrangements, and secured land ownership title played a key role in mitigating the likelihood of income loss.

Child mental health and educational attainment: Longitudinal evidence from the UK

There has been a recent spike in children's mental health problems and lost learning due to the Covid-19 pandemic. This study assesses the relationship between social, emotional and behavioural problems and academic attainment across the whole of mainstream schooling (ages 5–16). It uses a rich longitudinal dataset from the UK linked to national data on school records (N = 7,219), individual fixed effects, and repeated measures of mental health and attainment. It finds that within-individual changes in mental health across childhood predict changes in attainment, with hyperactivity and inattention the behaviours most strongly linked to adverse attainment outcomes. These difficulties are disproportionately seen in summer-born boys and those from disadvantaged backgrounds. The negative relationship with attainment strengthens as children progress through school, and affects children with problems mild enough that they would never be diagnosed. Schools and health services can gain important insights about a child's educational trajectory based on brief behavioural information obtained from parents.

Exploring the structure of college students' adaptability by using cross-lagged path analysis: The role of emotional adaptability.

Adaptability is an important psychological trait for college students. However, the components of adaptability contained significant inconsistencies in previous studies. On the one hand, there were discrepancies among the adaptability dimensions. On the other hand, significant inconsistencies were found in the connections among different aspects of adaptability. Therefore, the current research aimed to investigate the latent relationship among various components of adaptability. To achieve this, 565 volunteers were recruited to complete a 5-min cross-sectional survey. Subsequently, 402 participants were recruited to complete an 8-min longitudinal survey. The current study comprised two sub-studies: Study 1 utilized a structural equation model to examine the relationship between various dimensions of adaptability in a cross-sectional dataset, while Study 2 employed the cross-lagged panel model to validate the latent relationship between emotional adaptability and other types of adaptability using a longitudinal dataset. Results from the cross-sectional study indicated significant associations between emotional adaptability and other types of adaptability, with coefficients ranging from .231 to .588. The longitudinal study revealed that emotional adaptability at Time 2 and 3 could be predicted by learning adaptability, professional adaptability, and economic adaptability at Time 1 and 2. Consequently, the research concluded that individuals' emotional maladjustment could be predicted by maladaptive difficulties in learning, professional settings, homesickness, interpersonal relationships, and economics.