The study recruited consecutive patients from January 2020 through June 2024. Participants were eligible if they were 18 to 80 years old, had type 2 diabetes and had not received glucose-lowering medication. Researchers excluded people with type 1, gestational or secondary diabetes, as well as conditions independently capable of disturbing body composition or bone metabolism, including active malignancy, end-stage organ failure and malnutrition.

The final clustering analysis included 185 participants after observations with missing body-composition data and statistical outliers were removed. Participants were also assessed for glucose regulation, pancreatic function, liver enzymes, kidney function, uric acid, blood pressure and several bone-related markers. The analytical model classified the cohort into four groups.

Cluster 1 contained 42 participants and combined low fat mass with low lean mass. Cluster 2 was the largest group, with 71 participants, and was 98.6% male; it combined preserved muscle mass with a high percentage of body fat and the highest android-to-gynoid ratio, indicating a central-adiposity pattern. Cluster 3 comprised 49 participants, 98.0% of them female, and had high adiposity with relatively low lean mass.

Because muscle function was not measured, the investigators did not characterize this pattern as a clinical diagnosis of sarcopenia. Cluster 4 contained 23 participants and had the highest total, fat and lean mass as well as the greatest visceral adipose tissue. K-means clustering performed better than hierarchical clustering in internal validation, but its silhouette width was only 0.272, indicating limited separation between groups.

All four clusters achieved bootstrap Jaccard indices above 0.85, while re-clustering with 30 less-correlated variables reproduced the original solution with an adjusted Rand index of 0.912. Sex was a dominant feature of the classification and complicated interpretation. Cluster 2 was overwhelmingly male and Cluster 3 overwhelmingly female, making phenotype and sex statistically difficult to separate in those groups.

The researchers therefore standardized every DXA variable separately within men and women and repeated the clustering analysis. This removed the original association between cluster membership and sex, with the adjusted Rand index falling from 0.378 to 0.002, although agreement with the initial four-cluster solution was moderate, at 0.351. The sex-standardized analysis retained a central-adiposity group with an android-to-gynoid ratio of 1.3 that remained associated with alanine aminotransferase, gamma-glutamyl transferase, hip T-score, blood urea nitrogen, HDL cholesterol, triglycerides, HOMA-insulin resistance, osteoporosis, steatotic liver disease and hyperuricemia.

Separate analyses within men and women also found differences in adiposity, lean mass and metabolic outcomes, supporting the presence of residual heterogeneity beyond simple sex differences. Variance decomposition indicated that cluster labels captured information beyond sex for most of the DXA variables examined. For total fat mass, adding cluster to a sex-only model increased explained variance by 0.746, whereas adding sex to a cluster-only model increased it by 0.002.

The corresponding additional cluster contribution for android fat mass was 0.692. Contributions were more evenly divided for total lean mass, with values of 0.251 for cluster and 0.104 for sex, and for the android-to-gynoid ratio, with 0.047 and 0.068, respectively. Because the cluster labels were generated directly from the DXA variables, these calculations quantify the relative contribution of labels and sex rather than independently validate the existence of discrete clusters.

Regression adjustment for age, sex, smoking and alcohol consumption changed the cluster contribution to uric acid, alanine aminotransferase, hip bone density and blood urea nitrogen by no more than 0.016 in R-squared. Significant sex-by-cluster interactions nevertheless emerged for hip T-score and Z-score, showing that some bone-density associations differed between men and women. The four phenotypes did not differ significantly in HbA1c, fasting glucose, post-load glucose or HOMA-beta, and the observed HOMA-insulin-resistance difference did not survive Bonferroni correction.

During 75-g oral glucose tolerance testing, peak insulin and C-peptide secretion occurred later than 60 minutes in every cluster. Cluster 4 had the highest insulin and C-peptide areas under the curve and Cluster 1 the lowest, but neither area-under-the-curve measure remained significant after correction. Liver and renal markers provided more pronounced differences.

Alanine aminotransferase and gamma-glutamyl transferase differed significantly after correction and were highest in Cluster 4, whereas aspartate aminotransferase did not. Blood urea nitrogen also varied across clusters, while creatinine, estimated glomerular filtration rate, urinary albumin-to-creatinine ratio and urinary albumin excretion did not, indicating that the renal signal did not extend to a broad difference in intrinsic kidney function. Hip and femoral-neck bone mineral density differed across clusters after correction, with Cluster 4 recording the highest values and Cluster 1 the lowest; lumbar-spine T- and Z-scores did not differ.

Osteoporosis was present in 46.3% of Cluster 1. In a logistic model that pooled 20 multiply imputed datasets and adjusted for age, sex, smoking and alcohol consumption, Cluster 4 had lower odds of osteoporosis than Cluster 1, with an odds ratio of 0.16 and a 95% confidence interval of 0.03 to 0.92. Age, male sex and smoking were also independent correlates in that model.

The authors described the pattern as possibly consistent with differences in mechanical loading and endocrine influences associated with muscle and fat mass, but the cross-sectional data could not establish a mechanism. None of the measured bone-turnover markers—N-mid osteocalcin, P1NP, 25-hydroxyvitamin D or parathyroid hormone—retained significance after Bonferroni correction, so the density differences did not establish a distinct bone-turnover mechanism. After adjustment for age, sex, smoking and alcohol consumption, Clusters 2 and 4 had sharply higher odds of metabolic dysfunction-associated steatotic liver disease than Cluster 1.

The pooled odds ratio was 9.69 for Cluster 2, with a 95% confidence interval of 3.34 to 28.09, and 8.55 for Cluster 4, with an interval of 2.18 to 33.51. Cluster 3 did not differ significantly from the reference group, with an odds ratio of 2.44 and p = 0.142. Cluster 4 also had higher odds of hyperuricemia, with an odds ratio of 6.36 and a 95% confidence interval of 1.42 to 28.57, as well as higher serum uric acid levels.

Cluster 2 also had elevated serum uric acid, although its hyperuricemia estimate did not meet the significance threshold at p = 0.066. In the univariate analyses, cluster membership was associated with osteoporosis, steatotic liver disease and hyperuricemia; diabetic kidney disease, diabetic retinopathy, lower-extremity arteriosclerosis, carotid plaque, hypertension, coronary artery disease and dyslipidemia did not show significant univariate cluster associations. The researchers used multiple imputation to address substantial gaps in complication data, including 43.5% missingness for diabetic retinopathy and 41.4% for carotid plaque.

Results from complete-case analyses were directionally consistent for the three outcomes that initially showed associations. The diabetic-retinopathy analysis nevertheless included only eight confirmed cases among 105 participants with known status, precluding meaningful inference from that comparison. In nested models, adding cluster membership to BMI alone, BMI plus waist circumference, or BMI plus visceral adipose tissue area improved model fit for steatotic liver disease and blood urea nitrogen.

Cluster membership did not add significant incremental value for the other examined outcomes, indicating that its additional information was limited in this treatment-naive, newly diagnosed cohort. Important limitations included the single-center design, the small Cluster 4, modest separation between clusters, absence of external validation, sex-cluster non-separability for Clusters 2 and 3, and reliance on ultrasound rather than biopsy for diagnosing steatotic liver disease. Taken together, the findings show that DXA-based body-composition profiling distinguished clinically different patterns among people at the time of type 2 diabetes diagnosis, but the evidence does not establish a new diagnostic taxonomy or a causal pathway.

The most pronounced adjusted associations were higher odds of steatotic liver disease in the central-adiposity and high-fat-high-lean groups, along with higher odds of hyperuricemia in the high-fat-high-lean group. The low-fat-low-lean group had the highest osteoporosis prevalence, whereas the high-fat-high-lean group had lower adjusted odds of osteoporosis. These relationships persisted through statistical adjustment and imputation, yet they intersected with pronounced sex differences and, in several analyses, wide confidence intervals.

The cross-sectional results cannot determine whether body-composition patterns preceded metabolic disease or its complications, and prospective multicenter studies with larger sex-specific samples, systematic complication assessment and longer follow-up would be required to evaluate their prospective value beyond BMI, waist circumference and visceral fat.

Subject of Research: DXA-derived body-composition patterns and their metabolic associations in newly diagnosed type 2 diabetes

Article Title: Body Composition Phenotypes and Their Metabolic Associations in Treatment‐Naïve Patients With Newly Diagnosed Type 2 Diabetes: A DXA‐Based Cluster Analysis

Article References: Zhang, X., Zhang, R., & Jiang, T. (2026). Body Composition Phenotypes and Their Metabolic Associations in Treatment‐Naïve Patients With Newly Diagnosed Type 2 Diabetes: A DXA ‐Based Cluster Analysis. Endocrinology, Diabetes & Metabolism, 9(6), Article e70366. https://doi.org/10.1002/edm2.70366

Image Credits: AI Generated

DOI: 10.1002/edm2.70366

Keywords: type 2 diabetes, DXA, body composition, visceral fat, lean mass, metabolic dysfunction-associated steatotic liver disease, hyperuricemia, osteoporosis, cluster analysis, sex differences, Body, Composition

Developed as a supervised, cluster-based method, INSIGHT is intended for data that combines continuous and categorical features. Its central technical component is a computationally efficient mixed-distance metric, which allows the framework to compare records containing different types of information. The method also applies cluster-aware oversampling to address the extreme imbalance between fraudulent and non-fraudulent transactions while preserving the distribution structure of the data.

The evaluation used two financial datasets: European Credit Card, containing 284,807 transactions, and BankSim, containing 594,643 transactions. On European Credit Card, INSIGHT achieved the highest F1 score and area under the ROC curve among the compared approaches, including a tuned CatBoost model and several supervised base models. On BankSim, the framework achieved the highest area under the ROC curve and the highest precision among the compared methods.

The paper also reports that INSIGHT outperformed isolation forest and autoencoder-based anomaly detectors on both datasets. Alongside its predictive performance, the study provides theoretical guarantees concerning algorithmic convergence, cluster stability and scalable performance. Ablation studies assessed the individual contributions of the mixed-distance metric, cluster-aware oversampling and the choice of k-medoids variant.

Further experiments linked the framework’s theoretical properties with measured robustness and scalability. The study therefore combines formal analysis with empirical testing, although its evaluation is centered on these two financial datasets and the specified comparison methods. Interpretability is incorporated through cluster characterization and feature-importance analysis.

These outputs show how INSIGHT groups transactions and which variables contribute to its fraud classifications, allowing researchers and financial-security analysts to inspect the model’s behavior rather than rely only on an aggregate prediction score. The framework was published as a regular paper in the International Journal of Data Science and Analytics , Volume 22, as article 347, with a stated publication date of 20 October 2026.

Subject of Research: Scalable and interpretable cluster-based fraud detection in mixed-feature financial transaction data

Article Title: INSIGHT: a scalable cluster-based framework with theoretical guarantees for interpretable fraud detection in mixed-feature data

Article References: Yousefimehr, B., Ghatee, M., Seifi, M. A., & Fazli, J. (2026). INSIGHT: a scalable cluster-based framework with theoretical guarantees for interpretable fraud detection in mixed-feature data. International Journal of Data Science and Analytics, 22(1), Article 347. https://doi.org/10.1007/s41060-026-01313-4

Image Credits: AI Generated

DOI: 10.1007/s41060-026-01313-4

Keywords: fraud detection, financial analytics, mixed-feature data, cluster analysis, oversampling, machine learning, model interpretability, class imbalance, European Credit Card dataset, BankSim dataset, INSIGHT, scalable

The study, led by Tania Álvarez-Yates of the University of A Coruña and Óscar García-García of the University of Vigo, tackled a persistent methodological problem in athlete profiling. Most existing models are supervised: they train algorithms to predict performance, which conflates what an athlete is with what an athlete achieves. The team instead banned all outcome variables from the analysis. Race times over 1000, 500 and 200 metres, rankings, and medal standings were stripped out before clustering began, leaving a purely phenotypic feature space of body composition, muscle contractile properties, mobility, maximal and explosive strength, and canoe-specific neuromuscular data. Any structure the algorithms found would therefore reflect intrinsic biological variation rather than who happened to win.

The participants, twelve men and nine women aged 15 to 36, all competed at international level in the C1 category, the demanding single-blade canoe class where athletes kneel on one knee and paddle exclusively on one side. Their laboratory battery was exhaustive: bioelectrical impedance analysis for body composition, tensiomyography for the contractile properties of key paddling muscles, one-repetition maximum bench press and bench pull, shoulder and hamstring mobility tests, the Upper Quarter Y-Balance Test, and single-arm explosive rowing in a canoe-specific posture. The centrepiece was functional electromechanical dynamometry, or FEMD, a technique that measures force, power, work and impulse in positions that mimic the actual paddle stroke, including a maximal isometric test and an incremental resistance test taken to voluntary failure.

Before any clustering could happen, the raw data demanded careful handling. The researchers applied asymmetric winsorization, capping low values at the 10th percentile and high values at the 95th to preserve genuinely exceptional athletes while damping measurement artifacts, followed by a Yeo-Johnson power transformation to reduce skew and robust scaling based on medians and interquartile ranges. Two complementary algorithms, K-Means and hierarchical clustering with Ward linkage, were then run on the full scaled matrix, with exploratory PCA and t-SNE projections used only for visualization. Internal validation metrics, including the silhouette coefficient, the Davies-Bouldin index and the elbow method, converged on three clusters as the most defensible partition, and the two algorithms agreed moderately, with an Adjusted Rand Index of 0.591.

The three clusters that emerged were striking. The largest, containing ten athletes, was 90 percent female and was labelled the Female-Dominant Phenotype. The other two, comprising seven and four athletes respectively, were exclusively male. The sex-cluster association was overwhelming, with a chi-square statistic of 18.90 and a Cramér’s V of 0.949, indicating near-perfect coupling. The researchers are careful to frame this as normative sexual dimorphism rather than a performance hierarchy: the female-dominant cluster showed higher body fat, lower muscle mass, lower bone mass and reduced absolute force output, all consistent with well-established physiological differences between the sexes.

Yet the more intriguing story lies within the male subgroup. The High-Neuromuscular Male Phenotype and the Moderate-Neuromuscular Male Phenotype were statistically indistinguishable across all fifteen top discriminant variables, from peak eccentric force to mechanical work to repetitions to failure. The single variable that separated them was the sustain time of the latissimus dorsi on the non-paddle side, a tensiomyography measure of muscle contraction duration. Athletes with sustain times at or below roughly 152 milliseconds fell into the high-neuromuscular group, hinting at faster, more explosive contractile properties in the key pulling muscle of the non-dominant side, an asymmetry directly attributable to the unilateral demands of C1 technique.

Across the full 281-variable battery, neuromuscular FEMD measurements dominated the discrimination between clusters, accounting for 75.9 percent of the cumulative importance among the top fifteen features identified by a Random Forest Classifier. Peak force in the incremental test was the single most powerful discriminator, followed by repetitions to failure and total mechanical work. The female-dominant cluster produced roughly 40 percent less peak force and 63 percent less total work than the male clusters, and these gaps persisted, though attenuated, even after normalizing to body weight, consistent with sex-dependent differences in muscle fibre composition and hormonal milieu that mass scaling cannot erase.

The team then distilled the entire analysis into an interpretable decision tree with just two thresholds. First, any athlete producing peak eccentric force of 39.48 newtons or less in the canoe-specific test was assigned to the female-dominant cluster. Athletes clearing that bar were then split on latissimus dorsi sustain time, with faster contractions indicating the high-neuromuscular male phenotype. This shallow rule achieved 100 percent accuracy on the training subset and 71.4 percent on the held-out test cases, significantly above the 33.3 percent expected by chance, and correctly classified 19 of 21 athletes overall. The two misclassifications involved athletes sitting near the decision boundaries, which the authors interpret as genuine phenotypic ambiguity rather than model failure.

The caveats are substantial, and the authors are unusually candid about them. The silhouette coefficient of 0.056 is far below conventional thresholds for well-defined clusters, meaning the three groups describe overlapping gradients rather than discrete categories. The seven-athlete test partition yields a confidence interval stretching from 29 to 96 percent accuracy, so no strong claim of prospective generalization is statistically justified. Sex is structurally confounded with cluster membership, the age range spans markedly different developmental stages, and the smallest cluster contains only four athletes, raising the possibility that it is an algorithmic artifact rather than a true subtype. Bootstrap resampling further showed that fewer than half of the top-ranked discriminant variables kept their ranking under resampling, a hallmark of high-dimensional, small-sample instability.

Even so, the work points toward a compelling future for talent identification and training individualization in canoeing. A laboratory protocol that classifies an athlete into a phenotypic profile using just two metrics, eccentric force and a muscle contraction timing measure, could eventually inform sex-stratified benchmarks, cluster-specific strength programming, and longitudinal monitoring of how athletes move between profiles across training macrocycles. The researchers stress that external validation with larger, sex-balanced cohorts is required before any consequential decisions rest on these rules. But the core demonstration stands: even among athletes already good enough to represent their countries, hidden structure exists in the body that has nothing to do with race results, and unsupervised machines can find it.

Subject of Research: Unsupervised machine learning phenotypic clustering of elite flatwater canoeists based on neuromuscular and anthropometric variables

Article Title: Unsupervised phenotypic clustering, inferential characterization, and discriminant classification of elite flatwater canoeists: neuromuscular and anthropometric determinants

Article References: Álvarez-Yates, T., García-García, Ó., Cuba-Dorado, A., Bouza-Rodríguez, J.-B., Comesaña-Campos, A., & Cerqueiro-Pequeño, J. (2026). Unsupervised phenotypic clustering, inferential characterization, and discriminant classification of elite flatwater canoeists: neuromuscular and anthropometric determinants. Frontiers in Physiology, 17, Article 1949031. https://doi.org/10.3389/fphys.2026.1949031

Image Credits: AI Generated

DOI: 10.3389/fphys.2026.1949031

Keywords: elite canoeists, flatwater sprint canoeing, unsupervised machine learning, phenotypic profiling, functional electromechanical dynamometry, neuromuscular capacity, sexual dimorphism, cluster analysis, talent identification, tensiomyography, body composition, exercise physiology

The study, published in PLOS One by Jaeho Lee, Kyoungsik Jeong, Youngseop Lee, and Younghwa Baek, drew its data from the Korean Medicine Daejeon Citizen Cohort, a community-based longitudinal study that follows adults in the city of Daejeon. Rather than treating each measurement as an isolated number, the team asked whether people move along recognizable paths as their bodies change. To do this, they applied k-means cluster analysis, an unsupervised algorithm that groups observations based on similarity. The clustering was performed on two-year changes in six body-shape measures: body mass index, waist circumference, hip circumference, waist-to-hip ratio, waist-to-height ratio, and total body weight. The method is simple in principle but powerful in effect, because it allows patterns of change to emerge from the data rather than being imposed by predetermined categories such as normal weight, overweight, or obese.

Three clusters emerged from the analysis, and their proportions are striking. The largest group, comprising 48 percent of participants, was labeled Stable, showing minimal change across all six measures over the two-year window. The second group, 31 percent of the cohort, fell into an Adiposity-Increasing pattern, characterized by growth in waist circumference and related markers of central fat accumulation. The remaining 21 percent formed an Adiposity-Decreasing cluster, in which waist and hip measurements moved downward together. The fact that nearly a third of a general adult population shifted toward increasing adiposity in just two years underscores how dynamic body shape truly is, and how quickly the distributions of fat that matter for metabolic risk can evolve in midlife.

Central adiposity, the accumulation of fat around the abdomen, has become a focal point of cardiometabolic research because it behaves differently from fat stored elsewhere. Visceral fat, which wraps around internal organs, is metabolically active in ways that promote insulin resistance, elevated blood pressure, and chronic low-grade inflammation. This is why waist circumference, waist-to-hip ratio, and waist-to-height ratio have repeatedly outperformed body mass index in predicting type 2 diabetes, cardiovascular disease, and mortality. Body mass index, being a simple ratio of weight to height squared, cannot distinguish between a person whose weight reflects dense skeletal muscle and one whose weight reflects abdominal fat, nor can it detect the gradual loss of muscle that accompanies aging. The Korean study was designed precisely to address these blind spots by monitoring shape and composition simultaneously.

The researchers went a step further by connecting the clusters to underlying body composition. At baseline, cluster membership was significantly associated with body fat mass, meaning that the amount of fat a person carried before the study began already helped predict which trajectory their body shape would follow. More importantly, the changes in body fat mass and skeletal muscle mass over the follow-up period showed strong associations with cluster assignment. This dual signal, fat gain and muscle change moving in tandem with shape change, supports a model in which adiposity-related trajectories are not merely about excess energy storage but about the shifting balance between fat and lean tissue. A person can gain waist circumference while losing muscle mass, a combination that is metabolically worse than fat gain alone, and the cluster approach makes such patterns visible in a way that single measures cannot.

The lifestyle findings are where the study acquires its most practical relevance. Using multinomial logistic regression, a statistical method for modeling outcomes with more than two categories, the researchers tested which baseline and follow-up variables predicted membership in each cluster. Two factors stood out for the Adiposity-Increasing group. The first was a clinically meaningful worsening of sleep quality, defined as an increase of at least 2.5 points on the Pittsburgh Sleep Quality Index, a validated questionnaire that scores sleep across seven dimensions including duration, latency, disturbances, and daytime dysfunction. The second was a reduction in physical activity as measured by the Global Physical Activity Questionnaire, an instrument that captures work-related, transport-related, and recreational activity. Both associations remained significant after accounting for other variables, pointing to sleep and movement as modifiable levers that may help steer people away from the harmful trajectory.

The link between sleep and adiposity has accumulated considerable mechanistic support in recent years. Short or fragmented sleep disrupts the hormonal regulation of appetite, raising ghrelin, the hormone that stimulates hunger, and lowering leptin, the hormone that signals satiety. Poor sleep also increases insulin resistance, alters food preferences toward energy-dense options, and may reduce the energy expended through spontaneous physical movement the following day. Sleep quality deterioration is often a gradual process tied to stress, irregular schedules, and aging, which makes it easy to overlook in clinical practice. The finding that even a moderate worsening on a standardized sleep index tracks with increasing central adiposity suggests that clinicians monitoring waist circumference should also be asking patients about how they are sleeping.

Physical activity operates through more obvious but equally important channels. Skeletal muscle is the body’s largest site of glucose disposal, and regular activity maintains muscle mass and insulin sensitivity while increasing total energy expenditure. When activity declines, muscle mass tends to erode, resting metabolic rate falls, and the same caloric intake produces a greater fat surplus. The co-occurrence of reduced activity and increasing adiposity in the same cluster therefore reflects a coherent physiological story: less muscle-stimulating movement, more fat accumulation, and a body shape that shifts outward at the waist. The Korean data reinforce the idea that interventions aimed at preventing central obesity should treat exercise not simply as a weight-loss tool but as a body-composition intervention that protects lean tissue.

The study has limitations worth keeping in view. It was conducted in a single Korean city, and body-shape trajectories may differ across populations with different diets, occupational structures, and genetic backgrounds. The observational design means that associations between sleep, activity, and cluster membership do not prove causation; it is possible, for example, that early metabolic changes drive both poor sleep and reduced activity rather than the reverse. Even so, the two-year follow-up, the use of multiple correlated shape measures, and the integration of body-composition data give the findings a depth that cross-sectional studies of a single measure cannot match. Because sleep and activity are modifiable, the results translate readily into intervention targets even under the more conservative causal reading.

The broader message is one that resonates with a growing movement in obesity science: replacing the single-number mentality with an integrative view of body shape, body composition, and lifestyle. Two people with identical body mass indexes may sit on entirely different health trajectories depending on where their fat is stored, how much muscle they carry, how well they sleep, and how much they move. The Korean cohort’s three clusters, stable, adiposity-increasing, and adiposity-decreasing, provide a template for identifying people early, before their changing waistlines harden into diabetes or cardiovascular disease. And they offer reassurance of a kind: the trajectory most tied to risk is also the one most tied to behaviors that individuals can, with support and attention, change. Body shape, in other words, is not destiny written in a BMI number, but a dynamic process shaped nightly by sleep and daily by movement.

Subject of Research: Longitudinal clustering of body shape change and its association with body composition and lifestyle factors in Korean adults

Article Title: Lifestyle factors and clustered patterns of body shape change in Korean adults: A longitudinal observational study

Article References: Lee, J., Jeong, K., Lee, Y., & Baek, Y. (2026). Lifestyle factors and clustered patterns of body shape change in Korean adults: A longitudinal observational study. PLOS One, 21(10), e0360853. https://doi.org/10.1371/journal.pone.0360853

Image Credits: AI Generated

DOI: 10.1371/journal.pone.0360853

Keywords: obesity, body shape, waist circumference, body mass index, sleep quality, physical activity, body composition, skeletal muscle mass, cluster analysis, Korean adults, longitudinal study, adiposity

The study, conducted by Marlena Jolanta Piekut of Warsaw University of Technology, draws on the Polish Household Budget Survey compiled by Statistics Poland, combining annual aggregated statistics spanning 2004 through 2024 with detailed household-level microdata covering more than 28,000 households in each of 2023 and 2024. Poland is a particularly revealing case: Eurostat data show Polish households devote one of the highest shares of total spending to energy in the European Union, at 7.6 percent, reflecting both the country’s aging housing stock and its continued reliance on coal and other solid fuels for heating.

The technical core of the analysis rests on three complementary methods. Trend analysis and income elasticity calculations tracked how the energy burden evolved over twenty years, revealing that elasticity exceeded one during two crisis periods, 2008 to 2011 and 2021 to 2022, peaking at 1.77. In those windows, energy spending grew faster than income, meaning households could not easily cut back on an essential good. During calmer years, elasticity hovered near zero or turned negative, consistent with energy behaving as a necessity whose relative budgetary weight shrinks as incomes rise.

Cluster analysis of the microdata then sorted households into distinct consumption profiles. Two dominant groups emerged in both survey years: an electricity-and-gas profile covering roughly 52 to 58 percent of households, skewed toward rural areas, lower incomes, and lower educational attainment, and a district-heating profile covering about 42 percent, concentrated in cities among wealthier, better-educated households. In 2023 only, a small third cluster appeared, comprising 6 percent of households whose energy spending was 83.8 percent solid fuel, overwhelmingly coal and firewood, and these households were poorer, more rural, and less educated than the rest.

The most consequential methodological move was the construction of a multidimensional Energy Poverty Index combining four binary dimensions: an energy cost burden exceeding 10 percent of disposable income, low household income, inability to keep the home adequately warm, and arrears on energy bills. Households meeting at least two criteria were classified as energy poor. The results were striking: 11.4 percent of Polish households met the multidimensional definition, while nearly 30 percent more exhibited a single symptom, placing them at elevated risk. In total, more than 40 percent of households showed at least one manifestation of energy vulnerability.

Logistic regression identified the structural drivers behind this risk. Reliance on non-labor income nearly doubled the odds of multidimensional energy poverty, with an odds ratio of 1.78, and heating with a solid-fuel stove increased the odds by 43 percent. Education showed a steep protective gradient: relative to households whose head had at most lower-secondary schooling, basic vocational education cut the odds to 0.65, secondary education to 0.39, and higher education to 0.27. Household composition mattered sharply as well, with single mothers facing roughly double the odds and couples with four or more children about 2.1 times the odds of the reference group.

Disposable income declined monotonically with energy poverty severity, from an average of 4,359 zloty per capita among households with no symptoms to 2,718 zloty for those with one and just 1,588 zloty for the multidimensionally poor. The at-risk group sat closer to the energy-poor than to the secure on nearly every characteristic, sharing the same risk factors, including solid-fuel heating and dependence on non-labor income, which the author argues makes them a priority target for preventive intervention before deprivations accumulate.

A leave-one-dimension-out sensitivity analysis tested whether these findings were artifacts of the index’s construction. Each of the four dimensions was excluded in turn and the regression re-estimated. The core predictors, employment status, education, household composition, and marital status, remained stable in direction and magnitude across all four specifications, with model fit actually improving in some cases, indicating that no single dimension was disproportionately driving the conclusions.

Perhaps the study’s most important warning concerns interpretation. A declining expenditure share, the author cautions, should not be read as improving welfare. Energy-poor households may respond to rising costs not by spending more but by underheating their homes and forgoing essential energy services, so a low energy share can signal unmet needs rather than comfort. Expenditure-based indicators, including the single income-cost measure adopted in Poland’s draft Social Climate Plan, systematically underestimate the problem by overlooking material deprivation and housing conditions.

The policy implications point toward integration rather than any single lever. Translating the odds ratios into population effects, the study estimates that shifting all solid-fuel households to alternative heating would reduce multidimensional energy poverty prevalence by only about 0.4 percentage points, and no single-factor intervention exceeds roughly 0.6 points. Meaningful reduction, the analysis concludes, requires coordinated action combining targeted social protection with investments in residential energy efficiency, housing modernization, low-carbon heating, and labor-market activation, so that the energy transition does not deepen the inequalities it is meant to resolve.

Subject of Research: Multidimensional energy poverty and household energy expenditure patterns in Poland

Article Title: Household energy expenditures and multidimensional energy poverty in Poland: patterns, determinants, and policy implications

Article References: Piekut, M. J. (2026). Household energy expenditures and multidimensional energy poverty in Poland: patterns, determinants, and policy implications. Frontiers in Environmental Science, 14, Article 1950882. https://doi.org/10.3389/fenvs.2026.1950882

Image Credits: AI Generated

DOI: 10.3389/fenvs.2026.1950882

Keywords: energy poverty, Poland, household energy expenditure, multidimensional index, logistic regression, cluster analysis, solid-fuel heating, energy affordability, Household Budget Survey, energy transition, thermal deprivation, social policy

The research, published in BMC Psychology by Tomasz Kowalski of Cardinal Stefan Wyszyński University and the Institute of Sport in Warsaw, and Joanna Gorgol-Waleriańczyk of VIZJA University, took a person-centered approach to a question that has long been studied in athletes but rarely in the coaches who support them. Rather than averaging out how perfectionism or coping strategies relate to wellbeing across a whole sample, the researchers asked how these traits naturally co-occur within individuals. That distinction matters, because the psychological profile of a coach who sets impossibly high standards for himself while also believing that everyone around him demands perfection is likely very different from that of a coach who simply holds himself to exacting standards.

A total of 303 strength and conditioning coaches completed validated questionnaires measuring perfectionism, coping strategies, depressive and anxiety symptoms, and life satisfaction. The team then applied k-means cluster analysis, a statistical technique that groups individuals based on how similar their response patterns are across multiple dimensions simultaneously. The method searches for natural groupings in the data, allowing profiles to emerge from the responses themselves rather than being imposed by the researchers in advance. This approach has become increasingly popular in psychology precisely because it captures the way real people combine traits, rather than treating each characteristic as an independent variable with its own separate effect.

Three distinct profiles emerged from the analysis. The first group, labeled High-Strain Coaches, scored high on both self-oriented perfectionism, the tendency to demand flawlessness from oneself, and socially prescribed perfectionism, the belief that significant others expect perfection from them. The second group, the Internal Critics, combined high self-oriented perfectionism with a strong tendency toward self-blame when things go wrong. The third group, the Sustainable Professionals, showed lower levels of perfectionism overall and low self-blame, a combination that on its face suggests a healthier relationship with a demanding job.

When the researchers compared mental health outcomes across the three clusters, the differences were statistically significant across every measure examined. Both the High-Strain Coaches and the Internal Critics reported substantially higher levels of depressive and anxiety symptoms than the Sustainable Professionals. But the Internal Critics stood out in one additional and troubling way: they also reported the lowest life satisfaction of any group. In other words, the combination of perfectionism directed inward and a coping style built on self-criticism appears to be the most corrosive profile of all, eroding not just mood and anxiety levels but the broader sense that one’s life is going well.

The findings align with a well-established body of theory on perfectionism. Psychologists have long distinguished between perfectionistic strivings, the drive toward high personal standards, and perfectionistic concerns, the fear of failure and the sense that others demand the impossible. Self-oriented perfectionism can coexist with genuine achievement and even satisfaction when paired with flexible coping, but socially prescribed perfectionism, in which pressure is perceived as coming from outside, is consistently associated with poorer mental health. The present study adds a coaching-specific dimension to this picture, suggesting that the culture of strength and conditioning, with its emphasis on precision, accountability, and visible results, may amplify the perceived external pressure that makes socially prescribed perfectionism so damaging.

The coping dimension is equally important. Self-blame as a coping strategy, sometimes called self-criticism in the coping literature, involves attributing failure to one’s own character rather than to circumstances, strategy, or chance. Decades of research have linked this style to depression and anxiety across many populations, and the current findings suggest that when it combines with high personal standards, the result is a profile in which coaches are simultaneously demanding more of themselves and punishing themselves more harshly when they fall short. The Sustainable Professionals, by contrast, appear to hold high standards without the punitive internal response, a combination that seems to protect both mood and life satisfaction.

What makes this study particularly timely is the growing recognition that mental health in sport cannot be understood by looking at athletes alone. Strength and conditioning coaches occupy a peculiar position in the sporting hierarchy: they are authority figures to the athletes they train, subordinates to head coaches and performance directors, and often the first to be blamed when results disappoint. They work long and irregular hours, travel extensively, and frequently absorb the emotional labor of managing athletes’ frustrations while having few outlets of their own. If a substantial proportion of this workforce carries psychological profiles associated with elevated depressive and anxiety symptoms, the implications extend beyond individual suffering to the quality of support that athletes receive.

The authors argue that their results point toward targeted rather than generic interventions. Because the risk is concentrated in specific profiles, support programs should address both organizational contributors to perceived external pressure, such as unrealistic workload expectations and cultures of blame, and the internally driven patterns of perfectionism and self-critical coping that turn external demands into internal suffering. Cognitive approaches that help coaches reframe failure, distinguish between high standards and perfectionistic concerns, and develop more adaptive coping strategies could be particularly valuable for the Internal Critics, whose combination of self-imposed pressure and self-punishment places them at the greatest risk.

For the sporting world at large, the study serves as a reminder that the people who build the bodies of elite athletes are themselves human, and that the same psychological forces that drive excellence can, in the wrong combination, quietly undermine the very professionals the industry depends on. As mental health becomes an increasingly central concern across sport, the coaches who stand behind the athletes may finally be getting the attention their own wellbeing deserves, and this research offers a concrete map of where that attention is most urgently needed.

Subject of Research: Mental health profiles linked to perfectionism and coping strategies in strength and conditioning coaches

Article Title: Who is at risk? Mental health and perfectionism-coping profiles in strength and conditioning coaches

Article References: Kowalski, T., & Gorgol-Waleriańczyk, J. (2026). Who is at risk? Mental health and perfectionism-coping profiles in strength and conditioning coaches. BMC Psychology. https://doi.org/10.1186/s40359-026-05602-2

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05602-2

Keywords: strength and conditioning coaches, perfectionism, coping strategies, depressive symptoms, anxiety symptoms, life satisfaction, mental health, sport coaching, cluster analysis, self-blame, coach wellbeing, BMC Psychology

The study focused on first-year college students in Shandong Province, a large and demographically diverse population at a life stage when mental health vulnerabilities often surface. Moving away from home, facing academic pressure, and navigating new social worlds make the transition to college a well-documented risk period for psychological distress. Previous research has repeatedly shown that physical activity can alleviate mental health problems in adolescents, but comparatively little work has asked which specific components of physical fitness matter most for emotional well-being. That gap is what the University of Jinan team set out to close, and their answer challenges the assumption that all fitness abilities contribute equally to psychological strength.

Methodologically, the researchers took a two-track approach. On the physical side, they used performance data obtained through China’s National Student Physical Fitness Standard, the country’s official measurement framework for student health, as the yardstick for each student’s physical fitness level. This standard captures a battery of abilities, including body mass index, lung capacity, sit-and-reach flexibility, and other performance scores that together map the functional capacities of the body. On the psychological side, the team selected mature, validated scales to measure mental toughness, often described as psychological resilience, and emotion regulation, the self-efficacy students feel in managing their own emotional states. Questionnaires were distributed using group random sampling, a design intended to produce a sample representative of the broader first-year population rather than a convenience cluster of volunteers.

The analytical centerpiece of the study was cluster analysis, a statistical technique that groups individuals based on the similarity of their scores across multiple dimensions. Rather than treating all students as a single homogeneous mass, the researchers let the data reveal natural subgroups. The result was striking: college students sorted into three distinct categories of psychological emotional regulation capability. The team labeled these groups the Emotionally Volatile, the Exceptional Emotional Regulators, and the Steady Emotional Balancers. Each label describes a recognizable psychological archetype. The Emotionally Volatile group shows lower and less stable regulation capacity, the Exceptional Emotional Regulators sit at the high end with strong resilience and confident emotional management, and the Steady Emotional Balancers occupy a middle ground with consistent but moderate regulation skills.

This kind of group heterogeneity matters because it changes what the data can tell us. If the relationship between physical fitness and emotional regulation were identical for everyone, a single average correlation would suffice. But the cluster approach reveals that students are not interchangeable, and that the psychological landscape of a college campus is composed of qualitatively different emotional types. The researchers then asked whether these psychological groups also differed in their physical fitness profiles, applying chi-square tests to examine the association between cluster membership and fitness scores. The statistical threshold was clear: with a p-value below 0.05, the differences were unlikely to be products of random chance.

Two fitness measures stood out. Lung capacity, a direct index of respiratory function that reflects the efficiency of the cardiovascular and pulmonary systems, differed significantly among the three psychological categories. So did sit-and-reach flexibility, a measure of the extensibility of the lower back and hamstring muscles that serves as a proxy for overall musculoskeletal suppleness. The fact that these two abilities, rather than every component of the fitness battery, carried the significant signal is the study’s most technically interesting finding. It suggests that the psychological groups are not simply separated by general fitness or by body composition, but by specific functional capacities, one aerobic and one flexibility-based, that track with how well students regulate their emotions.

Why might lung capacity and flexibility connect to emotional regulation? The authors frame their conclusions cautiously, but the broader literature offers plausible mechanisms. Aerobic capacity is closely tied to regular physical activity, which is known to modulate stress physiology, influence mood-regulating neurochemical systems, and build the kind of physiological resilience that parallels psychological resilience. Flexibility, meanwhile, is often developed through stretching, yoga, and similar practices that combine slow controlled movement with attention to breathing and bodily awareness, activities frequently associated with reduced anxiety and improved self-regulation. The study does not claim that one causes the other, and the cross-sectional design cannot establish directionality, but the significant group differences provide a statistical foundation for designing targeted interventions and testing them experimentally.

The practical implications are where the research becomes genuinely actionable. The team recommends providing intervention and support as early as possible, ideally during the first year of college when students’ emotional regulation patterns are still forming and when the transition stress of university life is at its peak. More provocatively, they argue that physical activity programs should be set up in targeted and personalized forms according to students’ different psychological emotional types. A student in the Emotionally Volatile group might benefit from a different exercise prescription than a Steady Emotional Balancer, and the study’s cluster framework gives universities a data-driven way to sort students into intervention streams rather than guessing. This is the logic of precision health applied to campus recreation: measure, classify, and match the activity to the profile.

The study was supported by the National Social Science Fund of China under grant number 21BTY004, and it adhered strictly to the principles of the Helsinki Declaration, receiving ethical approval from the Science and Technology Office of the University of Jinan. Participants confirmed informed consent online, and the authors declare no competing interests. The research was published as an open access article, meaning the full cluster analysis, fitness comparisons, and scale validation details are freely available to any educator, coach, or researcher who wants to replicate or extend the approach in their own population.

For a science magazine audience, the takeaway is a compelling reframing of an old idea. We have long known that exercise is good for the mind, but this study adds technical texture to that claim: the mind-body connection is heterogeneous, structured into distinct psychological groups, and visible in specific fitness signatures like respiratory capacity and flexibility. As universities worldwide grapple with rising student mental health demands, the message from Shandong is that the fitness test sheet sitting in a campus database may already contain clues about which students are struggling to regulate their emotions, and that the smartest response is not more exercise in general, but the right exercise for the right emotional type, delivered early enough to make a difference.

Subject of Research: Psychology

Article Title: Group heterogeneity in the relationship of college students’ physical fitness levels on mental toughness and emotion regulation

Article References: Ma, W., Yang, Q., Li, X., Li, D., Qi, S., Ji, W., & Ma, C. (2026). Group heterogeneity in the relationship of college students’ physical fitness levels on mental toughness and emotion regulation. BMC Psychology. https://doi.org/10.1186/s40359-026-05525-y

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05525-y

Keywords: college students, physical fitness, mental toughness, emotion regulation, cluster analysis, lung capacity, flexibility, psychological resilience, BMC Psychology, mental health, Group, heterogeneity

The study was structured in two phases. In Phase 1, all 400 spring wheat genotypes were evaluated at the seedling stage under three moisture regimes: 100%, 70%, and 40% field capacity. A completely randomized design with three replicate pots per genotype-treatment combination was used, and ten morpho-physiological traits were recorded for each line. This seedling-stage screening served as the first filter, allowing researchers to identify genotypes that maintained growth under reduced water availability before committing resources to more expensive and time-consuming field trials.

The results of Phase 1 revealed the physiological toll that drought takes on young wheat plants. Mean seedling length declined from 40.0 cm under full moisture to approximately 29 cm under drought stress, a reduction that was statistically significant at P ≤ 0.01. Perhaps more telling was the response of proline content, an osmoprotectant that plants accumulate under water stress to protect cellular structures. Proline levels increased approximately 5.5-fold under drought conditions, also significant at P ≤ 0.01. Interestingly, no statistically significant difference was observed between the two drought levels for the growth traits, suggesting that once moisture dropped below a certain threshold, additional stress did not further impair seedling growth in a measurable way.

To make sense of the ten measured traits across 400 genotypes and three moisture regimes, the researchers turned to principal component analysis, a multivariate statistical technique that condenses correlated variables into independent axes of variation. The first two principal components jointly explained approximately 40% of the seedling-stage variation across all moisture regimes, while five components were required to explain more than 70%. This relatively even distribution of variance across multiple components indicated that drought adaptation at the seedling stage is not governed by a single dominant trait but rather by a combination of independent morpho-physiological characteristics.

Building on this multivariate framework, the team developed an integrated ranking system that combined standardized trait means, PC1 and PC2 scores, and hierarchical-cluster membership. Rather than relying on any single metric, this approach synthesized information from multiple dimensions of the phenotypic data to produce a holistic assessment of each genotype’s drought performance. From this ranking, 105 lines were advanced to Phase 2 field evaluation, representing roughly a quarter of the original pool. This sequential narrowing strategy exemplifies how controlled-environment screening can serve as an efficient pre-filter for field trials, reducing the logistical burden while preserving phenotypic diversity.

Phase 2 subjected the 105 selected lines to field-stage drought treatments at two critical developmental stages: drought at anthesis and drought at grain development, alongside a full-irrigation control. These stages are particularly vulnerable points in the wheat life cycle, as water stress during flowering can reduce grain set while stress during grain filling can diminish kernel weight and overall yield. The field evaluation provided a real-world test of whether seedling-stage tolerance translates into adult-plant drought resilience, a question that has long challenged plant breeders.

The field-stage results revealed a striking reorganization of the phenotypic structure under stress. Under full irrigation, the first principal component accounted for 60.96% of the phenotypic variation, indicating that a single dominant axis governed growth differences among genotypes in the absence of stress. However, under drought at anthesis, PC1’s contribution dropped to 22.47%, and under drought at grain development it fell to 30.26%. This redistribution of phenotypic variance across a larger number of independent axes under stress suggests that drought adaptation in adult wheat plants involves multiple independent traits rather than a single dominant characteristic, and that the genetic architecture of drought response differs fundamentally between well-watered and stressed conditions.

This stage-specific reorganization of the phenotypic structure carries important implications for breeding programmes. The authors caution against reliance on single-trait or single-stage selection, arguing that the multi-trait, multi-stage nature of drought adaptation demands a more comprehensive approach. A genotype that performs well at the seedling stage may not maintain that advantage under field conditions, and traits that correlate with yield under full irrigation may become decoupled under water stress. The pronounced shift in how phenotypic variance is distributed between seedling and field stages, and between different drought timing treatments in the field, underscores the complexity of drought as a breeding target.

The 105 lines identified through this sequential multivariate profiling represent a phenotypically diverse subset of Pakistani wheat germplasm suitable as parental material for a Line × Tester crossing programme aimed at drought resilience. The Line × Tester mating design is a well-established quantitative genetics approach that allows breeders to estimate combining ability and heterosis, providing insights into which parental combinations are most likely to produce superior drought-tolerant offspring. By ensuring that the selected parents span a range of phenotypic profiles rather than clustering around a single trait optimum, the multivariate approach maximizes the genetic diversity available for recombination in the breeding programme.

The germplasm used in this study was provided by the Pakistan Agricultural Research Council, the Wheat Research Institute in Faisalabad, and the Regional Agricultural Research Institute in Bahawalpur, reflecting a collaborative effort to harness national genetic resources for climate adaptation. The research was supported by the Recep Tayyip Erdoğan University Development Foundation, and the authors acknowledge TÜBITAK for granting a fellowship under the 2221 Sabbatical Scientist Support Programme. As drought intensifies across semi-arid wheat-growing regions from South Asia to the Middle East and beyond, the sequential multivariate profiling strategy demonstrated in this study offers a practical template for other breeding programmes seeking to identify climate-resilient germplasm from large collections. The work highlights that drought tolerance is not a single destination but a journey through multiple developmental stages, each with its own set of adaptive requirements, and that successful breeding for drought resilience must embrace this complexity rather than oversimplify it.

Subject of Research: Drought tolerance screening of bread wheat germplasm using multivariate phenotypic profiling

Article Title: Multivariate phenotypic profiling identifies drought-tolerant bread wheat germplasm through sequential seedling and field-stage screening

Article References: Zeeshan, M., Alam Khan, M., Ahmad, I., Normat Saydullayevich, X., Azhar Nadeem, M., Gou, J.-Y., Shehzad Baloch, F., & Tanveer Altaf, M. (2026). Multivariate phenotypic profiling identifies drought-tolerant bread wheat germplasm through sequential seedling and field-stage screening. BMC Plant Biology. https://doi.org/10.1186/s12870-026-10121-8

Image Credits: AI Generated

DOI: 10.1186/s12870-026-10121-8

Keywords: bread wheat, drought tolerance, germplasm screening, principal component analysis, cluster analysis, phenotypic profiling, plant breeding, climate resilience, Triticum aestivum, proline content, field capacity, semi-arid regions

The study’s central methodological claim deserves closer attention, because it challenges a habit that has become deeply embedded in how governments, journalists, and even researchers talk about national performance. Single indicators, whether a digital services score or a governance percentile, compress complicated realities into one number, and countries with very different profiles can end up side by side in a ranking table. A nation might score well on connectivity while lagging badly in the quality of its online public services, or it might have strong rule-of-law institutions but weak digital infrastructure. When such countries are placed adjacent in a league table, observers naturally assume they are similar, which can lead to poor policy borrowing and misplaced expectations. Cluster analysis addresses this problem by asking a different question: rather than ranking countries along a single axis, which countries actually resemble one another across many dimensions at once?

To answer that question rigorously, the authors did not rely on a single algorithm. They applied two fundamentally different clustering techniques: k-means, a non-hierarchical method that partitions observations into a predetermined number of groups by minimizing within-cluster variation, and Ward’s method, a hierarchical approach that builds clusters step by step by merging the pairs that produce the smallest increase in total variance. The fact that both methods, which operate on quite different mathematical logic, converged on the same four-group structure is important. Agreement between independent clustering approaches is a standard test of robustness, because it suggests the identified groups reflect genuine structure in the data rather than artifacts of one particular algorithm’s assumptions. The addition of Principal Component Analysis further strengthened the analysis by reducing the many individual indicators into a smaller set of underlying dimensions before grouping the countries.

The four clusters that emerged carry evocative names that summarize their positions. The ‘Digital Leaders’ occupy the top tier, combining strong digital public services with high-quality institutions. The ‘Advanced Digitalisers’ follow closely, showing strong digital performance with somewhat less institutional maturity. The ‘Digital Followers’ trail in adoption and institutional measures alike, while the ‘Challenges Group’ faces the steepest obstacles on both fronts. This gradation matters because it shows that digital maturity and institutional quality are not randomly distributed across the European Union; instead, they tend to move together, forming a coherent geography of development patterns that persists regardless of which clustering technique is applied.

Among the governance dimensions examined, four stood out for their association with digital performance: regulatory quality, government effectiveness, rule of law, and control of corruption. These indicators all loaded heavily on the main governance component identified through the principal component analysis, meaning they capture a common underlying factor of institutional strength. This finding aligns with a substantial body of scholarship in public administration and development economics, which has long argued that capable, predictable, and honest institutions are preconditions for complex administrative undertakings. Digital government is precisely such an undertaking: it requires agencies to redesign processes, share data across organizational boundaries, maintain reliable systems, and sustain public trust over long periods. Where corruption distorts procurement, where regulations shift unpredictably, or where civil servants lack the mandate and skills to implement reforms, technology investments alone rarely deliver their promised returns.

The practical implications for policy are perhaps the most consequential part of the study. For countries in the ‘Challenges Group’, the authors recommend prioritizing anti-corruption measures, regulatory quality, and administrative capacity-building rather than concentrating exclusively on technological infrastructure. This reverses a common pattern in which digital transformation strategies emphasize hardware, broadband rollout, and platform development while treating institutional reform as secondary or optional. The evidence assembled here suggests that sequencing matters: without credible institutions, even well-funded digital programs may stall at the implementation stage or fail to achieve meaningful adoption by citizens and businesses. Conversely, member states that have paired their technology investments with sustained administrative reform appear to have reached a self-reinforcing position, where good governance enables digital services and effective digital services, in turn, reinforce trust in government.

It is worth considering why digital public services, among all the dimensions of digitalization, are so tightly coupled to institutional quality. Unlike private-sector digital products, government services carry obligations of legality, accountability, and equal treatment. An online portal for business registration or social benefits is not merely software; it is the visible face of administrative law and procedure. Building such systems requires inter-agency coordination that only effective governments can mandate, procurement processes that only clean institutions can keep honest, and legal frameworks that only sound rule-of-law environments can guarantee. Citizens, moreover, must trust that the data they submit will be handled properly, and trust is difficult to establish where corruption is perceived to be widespread. In this light, the statistical association found in the study is not surprising but rather confirms what practitioners in e-government have often observed anecdotally.

The choice of data sources also shapes what the study can reveal. DESI, compiled by the European Commission, tracks connectivity, human capital, technology use, and digital public services across the member states, providing a harmonized and regularly updated picture of digital maturity. The Worldwide Governance Indicators, produced by the World Bank, aggregate many separate assessments into six composite dimensions of governance, ranging from voice and accountability to political stability. Because both datasets are standardized across all EU countries, they permit comparisons that would otherwise be impossible with purely national statistics. At the same time, composite indices have known limitations: they summarize diverse underlying measurements, and their construction involves methodological choices that can influence results. The authors’ decision to use multiple clustering methods and dimension-reduction techniques can be read as a deliberate response to these limitations, testing whether the typology survives reasonable variations in analytical approach.

For the member states themselves, the typology offers a mirror that single rankings cannot provide. A country placed in the ‘Advanced Digitalisers’ group, for example, can see that its nearest peers share a particular combination of strengths and weaknesses, and can look to the ‘Digital Leaders’ for models of the institutional reforms that appear to accompany the transition to the top tier. Countries in the ‘Digital Followers’ group, meanwhile, may find that their most relevant benchmarks are not the continental frontrunners but peers facing similar starting conditions, whose recent experiences are more transferable. This peer-group logic is one of the quiet strengths of cluster analysis in comparative policy research: it replaces the intimidating single leaderboard with a map of realistic reference points.

The study also contributes to a broader conversation about how sustainability and digitalization intersect. Discover Sustainability, the journal in which the work appears, sits at the junction of environmental, social, and economic sustainability research, and the framing of the article reflects that orientation. Efficient and transparent public services, the authors argue in their abstract, foster a competitive business environment and social well-being by facilitating business expansion, job creation, and foreign direct investment. Digital government, in this view, is not an end in itself but an instrument for broader developmental goals. The finding that institutional quality underpins digital maturity adds a governance dimension to sustainability discussions, suggesting that anti-corruption efforts and administrative reform are not merely matters of political hygiene but foundations for the digital capabilities on which modern economies increasingly depend.

Several caveats are appropriate when interpreting results of this kind. Cluster analysis identifies patterns in data as they exist at a point in time; it does not by itself establish causal direction. The strong association between governance and digital maturity could reflect institutions enabling digitalization, digitalization strengthening institutions, or third factors, such as overall economic development and historical trajectories, driving both. Longitudinal research that tracks countries as they move between clusters over successive measurement periods would help disentangle these possibilities. Similarly, the four-cluster solution, while robust across the two methods employed, represents one reasonable summary of a complex landscape; alternative specifications of indicators or distance measures could yield somewhat different groupings. The authors’ transparency about their methods allows other researchers to test such variations.

Nevertheless, the consistency of the four-group structure across independent techniques, and the coherent pattern of governance loadings that accompanies it, give the typology considerable weight. For policymakers in the European Union, the message is sobering but actionable: the digital divide between member states is not primarily a technology gap that procurement budgets can close, but a governance gap that requires patient institutional work. For researchers, the study demonstrates the value of multidimensional, methodologically plural approaches to comparative benchmarking. And for anyone who has watched a national digital strategy falter despite ample funding, the findings offer an explanation rooted in evidence: sustainable digital transformation, the authors conclude, requires aligning technological investment with broader administrative reform, and the countries that have understood this are precisely the ones that now lead the field.

Subject of Research: Clustering of EU member states by digital public services maturity and institutional governance quality

Article Title: A four-cluster typology of the EU member states in digital public services and institutional governance using DESI and WGI data

Article References: Gurnasa, L., & Mazure, G. (2026). A four-cluster typology of the EU member states in digital public services and institutional governance using DESI and WGI data. Discover Sustainability. https://doi.org/10.1007/s43621-026-04895-y

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04895-y

Keywords: EU member states, cluster analysis, DESI, Worldwide Governance Indicators, digital public services, e-Governance, institutional governance, k-means clustering, Ward's method, principal component analysis, digital maturity, regulatory quality

Positive affect and negative affect — the twin engines of subjective well-being — matter enormously for college students. Positive affect refers to feelings of enthusiasm, activation, and alertness, while negative affect captures general subjective distress and unpleasant engagement. Decades of research have linked higher positive affect and lower negative affect to better physical health, stronger academic performance, and more successful campus lives. Crucially, the two are not simply opposites: they are partly independent of each other, meaning a person can be high on both, low on both, or any combination in between. That independence is exactly why the new study examined both simultaneously rather than treating mood as a single dial.

The researchers focused on social achievement goals — orientations toward social competence that describe what students are trying to accomplish in their social lives. Psychologists A. M. Ryan and S. S. Shim, who proposed the framework in 2006, distinguished three types. Social development goals center on genuinely improving social skills, deepening relationship quality, and building a richer social life. Social demonstration-approach goals center on displaying social competence and winning positive evaluations from others. Social demonstration-avoid goals center on avoiding situations that would expose one’s social competence as insufficient. Earlier studies had tied each of these goals separately to affect: social development goals correlated with higher positive affect, demonstration-avoid goals with higher negative affect, and demonstration-approach goals with elevated joy. But those studies used a variable-centered approach, examining one goal at a time while statistically holding the other two aside.

That limitation matters. People do not hold goals in isolation; they endorse multiple goals simultaneously, and the interplay among them may shape emotional outcomes in ways single-goal analyses cannot detect. The new study therefore adopted a person-centered approach, using cluster analysis to sort students into subgroups defined by their full goal profiles. No previous study had simultaneously examined positive and negative affect with this method among Japanese college students, and relatively few person-centered studies existed anywhere — one focused on Turkish adolescents aged 12 and 15, another on Korean high school students. The Japanese sample thus fills a real gap in the literature.

The team conducted an online survey in late June 2024 through a Japanese research panel, screening for current college enrollment. Valid responses came from 330 students with a mean age of 20.13 years, split evenly between 165 men and 165 women. Participants spanned all undergraduate years, from 25.5 percent freshmen to 29.7 percent seniors, with small numbers of fifth- and sixth-year students. Geographically, the largest share lived in the Kanto region around Tokyo (38.5 percent), followed by the Kinki region (21.8 percent), Chubu (13.6 percent), and Kyushu (10.0 percent), with smaller contingents from Chugoku, Tohoku, Hokkaido, and Shikoku. The study was approved by the Research Ethics Committees of Hijiyama University and Hijiyama University Junior College in June 2024.

Participants completed the validated Japanese version of the Social Achievement Goals Questionnaire, a 12-item scale with three four-item subscales rated on a five-point scale, and the Japanese version of the Positive and Negative Affect Schedule (PANAS), whose two eight-item subscales were rated on a six-point scale. Internal consistency was strong throughout: Cronbach’s alpha reached 0.74 for social development goals, 0.83 for demonstration-approach goals, and 0.71 for demonstration-avoid goals, while the PANAS subscales showed alphas of 0.91 for positive affect and 0.92 for negative affect. The researchers then applied Ward’s method of cluster analysis to standardized goal scores, using SPSS version 28 with a significance threshold of p < 0.05, and examined between-cluster differences in affect with one-way analyses of covariance controlling for sex.

Four clusters emerged. The first, containing 25 students, was labeled the low cluster because all three goal types scored low. The second, with 60 students, was the low social demonstration-approach goal cluster, in which social development goals were somewhat high, demonstration-approach goals were clearly low, and demonstration-avoid goals were average. The third, with 76 students, was the high cluster, scoring high on all three goals. The fourth and largest, with 169 students — just over half the sample — was the average cluster, with average levels of everything. Analyses of covariance confirmed the clusters differed robustly on each goal type, with effect sizes (partial eta squared) of 0.59 for social development goals, 0.55 for demonstration-approach goals, and 0.48 for demonstration-avoid goals. Two of these clusters — the average cluster and the low demonstration-approach cluster — had not appeared in comparable studies of Turkish and Korean students, suggesting that goal-profile distributions vary across populations.

The affective results were strikingly asymmetric. On positive affect, the low cluster scored far below everyone else: its adjusted mean of 17.66 fell roughly 8 to 10 points beneath the low demonstration-approach cluster (25.75), the high cluster (28.01), and the average cluster (26.06), with all three differences statistically significant at p < 0.001. On negative affect, however, the picture inverted. The low cluster again sat at the bottom — 17.88 versus 26.57 for the high cluster and 24.58 for the average cluster — but this time being low was the favorable position. The low demonstration-approach cluster (21.39) also scored significantly below the high cluster on negative affect. In short, students with weak social goals across the board reported the least enthusiasm but also the least distress, while students with strong goals reported high energy and high distress alike.

The authors offer a tentative explanation for the low cluster’s emotional flatness: these students may be less motivated to engage in social interactions with peers and therefore less likely to experience either kind of affect. That hypothesis, they note, requires empirical testing. The broader implication is that the most adaptive goal profile depends on how one defines emotional adjustment. If the criterion is high positive affect — the component most consistently tied to physical health and academic performance — then any combination of meaningful social goals beats having none. If the criterion is low negative affect, weak goals across the board look best. Because positive and negative affect are independent constructs, neither conclusion can stand alone; well-being research must weigh both.

Several caveats temper the findings. The participants were recruited through a commercial panel and do not constitute a representative sample of Japanese college students, so replication with larger and more diverse samples is needed. The study was also cross-sectional, meaning causality cannot be established: the researchers cannot rule out the possibility that a third variable explains the associations between goal clusters and affect, and experimental studies that manipulate social achievement goals would be required to test causal claims. The authors also acknowledge that the two novel clusters diverge from prior work and that understanding why populations differ in their goal profiles remains an open question. Even so, the study demonstrates the value of person-centered analysis for well-being science: by identifying typical goal combinations rather than isolating single goals, it reveals subgroups of students who may experience markedly different degrees of affective well-being, offering a more nuanced map of how social motivation and mood intertwine on campus.

Subject of Research: Social achievement goal profiles and their associations with positive and negative affect among Japanese college students

Article Title: Differences in positive and negative affect across social achievement goal clusters among Japanese college students

Article References: Kashiwagi, S., Horiuchi, S., Aoki, S., & Kira, Y. (2026). Differences in positive and negative affect across social achievement goal clusters among Japanese college students. Frontiers in Social Psychology, 4, Article 1674815. https://doi.org/10.3389/frsps.2026.1674815

Image Credits: AI Generated

DOI: 10.3389/frsps.2026.1674815

Keywords: social achievement goals, positive affect, negative affect, cluster analysis, college students, Japanese students, subjective well-being, PANAS, person-centered approach, social competence, emotional adjustment, campus psychology