The research tackles a problem that has long frustrated destination marketers: different groups tell different stories about the same place. Press Generated Content, or PGC, includes the travel journalism and professional media coverage that shapes how potential visitors imagine a destination. Tourist Generated Content, or TGC, captures what actual visitors post on social platforms after their trips, reflecting lived experience rather than marketing intent. Official Generated Content, or OGC, represents the messaging produced by government and institutional bodies responsible for promoting the city. When these three narrative systems align, a destination’s image is coherent and persuasive. When they diverge, tourists may arrive with expectations that the reality on the ground cannot meet, a phenomenon the researchers call destination image dissonance.

To measure this dissonance rigorously, the team analyzed content produced between 2013 and 2022, splitting the decade into a pre-pandemic phase from 2013 to 2019 and a pandemic-affected phase from 2020 to 2022. The methodological centerpiece is the Aitchison distance, a metric designed for compositional data, where values are expressed as proportions of a whole and must be interpreted relative to one another rather than in absolute terms. Ordinary distance measures can mislead when applied to such data, but the Aitchison distance respects the geometry of the simplex, the mathematical space in which compositional vectors live. This allowed the researchers to compute a single number that captures how structurally different the official narrative is from the tourist narrative in any given period.

The results are striking. In the pre-pandemic years, the Aitchison distance between official and tourist narratives stood at 19.574. During the pandemic-impacted phase, it jumped to 40.648, more than doubling. This means that the structural gap between what institutions were saying about Sanya and what tourists were actually experiencing and sharing became far more pronounced amid the disruption of COVID-19. The finding suggests that public health crises do not merely reduce tourism volumes; they can fundamentally reshape the relationship between institutional promotion and on-the-ground practice, pushing the two narrative systems further apart precisely when coordinated communication matters most.

The researchers examined the divergence across three dimensions: cognitive, spatial, and affective. On the cognitive dimension, they found notable consistency in one area, tourism and hotels, where all three content types converged on similar themes. But beyond that shared ground, the narratives split. Tourist Generated Content gravitated toward coastal scenery and food, the sensory and experiential elements that dominate personal travel storytelling. Press Generated Content and Official Generated Content, by contrast, emphasized policy elements, reflecting the priorities of institutions that frame tourism in terms of governance, planning, and economic strategy. This split reveals a fundamental difference in what each narrative system considers worth talking about when describing the same city.

The spatial dimension produced perhaps the most consequential finding. The study documented a misalignment between policy-driven spaces, the areas and developments that official narratives highlight, and the spaces tourists actually prefer and write about. This is not a trivial mismatch of emphasis but a systematic dissonance between institutional promotion and on-the-ground practices. When the places being marketed diverge from the places visitors actually value and occupy, the resulting experience can feel like a bait-and-switch, eroding trust and dampening the likelihood of return visits or positive word-of-mouth recommendations.

On the affective dimension, the study found that the emotion of enjoyment and romance dominated across content types, reinforcing Sanya’s positioning as a honeymoon and leisure paradise. Yet beneath this shared emotional vocabulary, the researchers identified a deeper asymmetry. Tourist Generated Content expressed unmet needs, surfacing complaints and disappointments that the glossy promotional picture does not accommodate. Official Generated Content, meanwhile, tended to suppress negative elements, presenting a curated emotional landscape that filters out friction. This dynamic means that the affective gap is not merely a difference in tone but a difference in what is permitted to exist within each narrative system at all.

The implications extend well beyond Sanya. Destination image research has long recognized that images are constructed through multiple channels, but quantifying the divergence between those channels has been methodologically difficult. By applying compositional data analysis and the Aitchison distance to cross-media representations, the study offers a template that other destinations could adopt to diagnose their own narrative health. A city that discovers its official and tourist narratives are drifting apart has an early-warning signal, one that can trigger targeted interventions before the gap translates into reputational damage or declining visitor satisfaction.

The researchers propose targeted strategies to narrow the discrepancies they measured. While the specifics of those strategies are grounded in the Sanya case, the underlying logic is broadly applicable: institutions need to listen to what tourists actually say, not just broadcast what they want tourists to hear. Closing the gap requires incorporating tourist-generated insights into official messaging, aligning promoted spaces with the places visitors genuinely value, and allowing official narratives to acknowledge imperfections rather than suppressing them. In an era where a single viral post can reshape perceptions faster than any advertising campaign, the cost of narrative dissonance has never been higher.

The pandemic years serve as a natural experiment in how crises stress-test the relationship between institutions and the publics they serve. That the Aitchison distance more than doubled during COVID-19 suggests that disruption amplifies pre-existing fractures rather than creating new ones. For Sanya, the findings arrive at a pivotal moment as the city works to rebuild its tourism economy. For destination researchers and marketers everywhere, the study demonstrates that the health of a destination’s image can be measured, monitored, and managed, provided the right mathematical tools are pointed at the right streams of content. The postcard, it turns out, is only as strong as the distance between it and reality.

Subject of Research: Measuring divergence between official, press, and tourist narratives of Sanya's tourism image using compositional data analysis

Article Title: Measuring destination image dissonance: A compositional data analysis of cross-media representations in Sanya’s Tourism

Article References: Li, X., Ding, J., Zhang, Y., & Hu, T. (2026). Measuring destination image dissonance: A compositional data analysis of cross-media representations in Sanya’s Tourism. PLOS One, 21(10), e0360383. https://doi.org/10.1371/journal.pone.0360383

Image Credits: AI Generated

DOI: 10.1371/journal.pone.0360383

Keywords: destination image, compositional data analysis, Aitchison distance, Sanya, tourism marketing, COVID-19, social media, tourist generated content, official generated content, narrative dissonance, Hainan, PLOS ONE

The research team focused on the La Esperanza Forest, a private reserve in El Carmen canton in Manabí province, within the Tumbes-Chocó-Magdalena biodiversity hotspot. This bioregion stretches from southern Panama along the Pacific coasts of Colombia and Ecuador into northwestern Peru, and it ranks as the ninth most biodiverse hotspot on the planet, holding roughly 11,000 plant species, about 3 percent of all known plants, on less than 0.2 percent of Earth’s land surface. Yet more than 85 percent of the Ecuadorian Chocó’s original habitat has been lost in recent decades, driven by livestock, agriculture, forest fires, and urban expansion. What remains is a fragmented archipelago of isolated rainforest patches, of which La Esperanza is a rare example that scientists can actually access and study.

The reserve itself is remarkably rich for its size. Spanning just 15.22 hectares, it shelters a canopy 20 to 30 meters tall with emergent giants such as Eschweilera awaensis and palms like Attalea colenda and Oenocarpus bataua. A preliminary botanical census documented around 200 plant species in a single hectare, many still unidentified, alongside 105 bird species, 13 amphibians, 10 reptiles, and 10 large mammal species. Of the reserve’s area, about 11.22 hectares are mature forest, while the remaining 4 hectares were once farmland and pasture. Since 2022, that disturbed section has been undergoing agrochemical-free restoration, with 118 native tree species planted between 2022 and 2024, livestock excluded, invasive species removed annually, and supplementary irrigation provided during dry months. No soil amendments or chemical inputs have been applied since restoration began.

To capture the microbial signature of each land-use condition, the researchers sampled three habitat types: the remnant reference forest, the early-stage restored parcel, and an adjacent degraded cattle pasture that had been fumigated before 2017 with atrazine and the systemic herbicide Tordon 101, then maintained under grazing and manual slashing ever since. Fieldwork took place over three days in late September 2024. Within each habitat, the team established two 10 by 10 meter plots, subdivided each into a grid of 25 two-meter subplots, and collected pooled soil aliquots from the corners of eight dispersed subplots per plot. After two forest samples failed to yield viable DNA, the final dataset comprised 46 soil samples from the top 15 centimeters of the soil profile, preserved on dry ice and stored at minus 20 degrees Celsius until analysis. Soil DNA sequencing was authorized by the Ecuadorian government under a genetic resource access contract, and the samples are now permanently archived in the National Bank of Genetic Resources at INABIO.

The molecular approach relied on 16S rRNA gene metabarcoding, a technique that reads a diagnostic stretch of bacterial DNA to identify which microbes are present and in what relative proportions. The team used Oxford Nanopore long-read sequencing on a GridION device, amplifying nearly the full-length 16S gene with primers 27F and 1492R, then filtering reads to keep only sequences between 800 and 2,000 base pairs. Taxonomic classification was performed with the Kraken2 k-mer algorithm against the NCBI 16S/18S reference database, followed by Bayesian abundance re-estimation with Bracken at the genus level. The result was a catalogue of 1,208 distinct bacterial genera across all sampled habitats, with rarefaction curves confirming that sequencing depth was sufficient to capture the taxonomic diversity present in each sample.

What emerged first was a picture of surprising continuity. A core set of dominant genera, including Bacillus, Haliangium, and Paenibacillus, persisted across all three habitats with stable relative abundances, suggesting that the fundamental biological scaffolding of these soils survives even under intense aboveground disturbance. But the researchers caution that the persistence of these generalist taxa should not be mistaken for evidence of ecological health. In fact, these dominant genera tended to be more abundant in the degraded and restored habitats, which may partly explain the descriptive reduction in community evenness observed there compared to the forest.

When the team analyzed the overall structure of the bacterial communities using compositional data analysis, a fundamentally different picture emerged. Because sequencing data are inherently compositional, meaning that the abundance of one taxon is only meaningful relative to all others, the researchers applied a centered log-ratio transformation through the ALDEx2 package, which uses Monte Carlo sampling from a Dirichlet distribution to estimate true relative abundances and technical variation. They then computed Aitchison distances, a mathematically robust metric for compositional data, and ran a permutational multivariate analysis of variance aggregated at the plot level to account for the nested spatial design. The omnibus test did not reach traditional statistical significance, a consequence of the highly conservative model with only six experimental units, but the effect size was striking: habitat condition explained over 66 percent of the total structural variance in the soil communities. Pairwise comparisons reinforced this pattern, with large effect sizes separating forest from restored soils and forest from degraded soils, while the restored and degraded plots clustered much closer together.

Principal component analysis made the separation visually unmistakable. The first principal component, accounting for 21.7 percent of the variation, completely partitioned the forest samples away from both the restored and degraded plots. The taxa driving this separation were revealing. Forest soils were strongly associated with genera such as Dinghuibacter, Ilumatobacter, Amaricoccus, Aminobacter, and Phyllobacterium, along with two uncultured lineages from the Saprospiraceae and Nitrosomonadaceae families. Many of these are Gram-negative bacteria previously isolated from forest soils or associated with tree roots and leaves, and some species of Phyllobacterium are symbiotic nitrogen-fixers used in microbial fertilizers. In contrast, the degraded and restored soils were characterized by genera including Sedimentibacter, Methylocystis, Chungangia, Fervidicella, and notably Rhodanobacter, a genus frequently linked to denitrification in contaminated and agricultural soils. The differential abundance analysis confirmed that Rhodanobacter was significantly enriched in the degraded environment, while the restored parcel showed significant enrichment of Fictibacillus and an uncultured Nitrosomonadaceae genus, and the forest maintained significantly higher levels of an uncultured Saprospiraceae genus compared to the restored soils.

Perhaps the most ecologically revealing finding came from partitioning beta diversity into its two components: species turnover, which reflects the replacement of one set of taxa by another, and nestedness, which reflects the sequential loss of taxa from a richer community. Using the Sørensen dissimilarity index, the researchers found that turnover overwhelmingly dominated the structural differences between habitats, with the turnover fraction ranging between roughly 0.15 and 0.30 while nestedness remained below 0.10 across all comparisons. This means that agricultural conversion and early restoration did not simply strip away pieces of the original forest microbiome; instead, they replaced it with an entirely different bacterial architecture. The turnover component alone explained over 64 percent of the variance in species replacement, with large effect sizes between forest and restored plots and between forest and degraded plots, but a much smaller effect size between restored and degraded plots. The nestedness component, by contrast, showed virtually no variance explained by habitat type, confirming that subset-loss dynamics played almost no role in shaping the observed differences.

The implications for restoration practice are sobering but not discouraging. The restored parcel, despite hosting 118 native tree species and two years of careful agrochemical-free management, clustered structurally closer to the degraded pasture than to the reference forest, lacking the high-loading structural drivers that define the forest soil microbiome. This decoupling of aboveground and belowground recovery is increasingly recognized across tropical systems, with recent studies showing that microbial community composition shifts distinctly during secondary succession while overall diversity does not necessarily parallel the recovery of plant diversity, and that the structural transition to a mature, forest-like metabolic network spans decades rather than years. The researchers emphasize that their findings represent a localized proof-of-concept within a single reserve rather than a universal model for the heterogeneous Chocó bioregion, and they acknowledge that the absence of soil physicochemical measurements such as pH, organic carbon, and nutrient levels limits mechanistic interpretation. Nevertheless, the study establishes a rigorous molecular baseline for long-term monitoring at La Esperanza and makes a compelling case that remnant forest patches are not just refuges for visible biodiversity but critical reservoirs of belowground microbial diversity that early-stage restoration cannot quickly replicate. The authors propose moving beyond static baselines to longitudinal plots tracked over decades, coupling DNA sequencing with biogeochemical measurements and transitioning to shotgun metagenomics to capture the full functional dimension of ecosystem recovery. For now, the message is clear: the invisible half of the forest is far harder to rebuild than the half we can see, and protecting what remains may be the most efficient restoration strategy of all.

Subject of Research: Soil bacterial community structure across remnant forest, restored, and degraded land uses in the Ecuadorian Chocó

Article Title: Soil metabarcoding reveals the distinct microbial signature of a remnant forest patch at a local restoration initiative in the Ecuadorian Chocó

Article References: Santillán-Sarmiento, A., Carrión-Olmedo, J. C., Salinas-Tapia, J., Lojan-Cueva, P., Flores, D., Reyes-Barriga, D., Jaramillo, L., Narváez, A. E., & Jarrín-V, P. (2026). Soil metabarcoding reveals the distinct microbial signature of a remnant forest patch at a local restoration initiative in the Ecuadorian Chocó. Frontiers in Forests and Global Change, 9, Article 1892271. https://doi.org/10.3389/ffgc.2026.1892271

Image Credits: AI Generated

DOI: 10.3389/ffgc.2026.1892271

Keywords: soil microbiome, 16S rRNA metabarcoding, Ecuadorian Chocó, tropical forest restoration, bacterial communities, beta diversity, compositional data analysis, biodiversity hotspot, La Esperanza Forest, Rhodanobacter, Phyllobacterium, soil health

The problem CoMeDA addresses begins with the nature of sequencing data itself. When researchers sequence amplicons from a microbial community, the read counts they obtain are inherently compositional: the abundance of each taxon is only meaningful relative to the others, because sequencing depth constrains the total. A species that appears to double in abundance may simply have declined less than its neighbors. Ignoring this compositional structure, the authors note, can produce spurious results in downstream analyses, a pitfall that has been documented repeatedly in the compositional data analysis literature yet is still not consistently handled in widely used microbiome platforms.

Compounding the issue is the batch effect problem. Multi-cohort studies, which combine samples processed at different times, in different laboratories, or on different sequencing platforms, frequently carry technical variation that can masquerade as biological signal. If a disease cohort happens to be sequenced in a different batch than the healthy controls, apparent differences in microbial composition may reflect the sequencing run rather than the disease. Batch effects, if not properly addressed, can confound the very signals researchers are trying to detect. Despite the availability of multiple microbiome analysis platforms, the authors argue, these two analytical considerations have not always been incorporated together as central, user-accessible components of a workflow.

CoMeDA treats the compositional structure of metabarcoding data and batch effects as coordinated components of one analytical pipeline rather than as optional add-ons. The platform accepts both raw FASTQ files and pre-classified taxa tables, making it compatible with short-read and long-read sequencing technologies alike. That flexibility matters as long-read platforms such as PacBio gain traction in microbiome research, since many existing tools were built around short-read data. For the crucial first steps of quality control and taxonomic assignment, users can start from raw reads or jump in with an already classified table.

At the statistical core of the platform is the centered log-ratio transformation, applied as the default normalization strategy. CLR transformation is a standard technique in compositional data analysis: it converts relative abundance data into log-ratios relative to the geometric mean of all taxa in a sample, placing the data in a space where ordinary statistical operations are more defensible. Before the transformation can be applied, however, the platform must contend with zeros, the many taxa that were simply not observed in a given sample. CoMeDA performs zero handling through probabilistic estimation, replacing true zeros with small estimated values so that log-ratios remain computable without distorting the underlying structure.

Once the data are normalized, CoMeDA applies automated batch effect correction that removes technical variation while preserving biological signals. The balance is delicate: overcorrection can erase genuine differences between conditions, while undercorrection leaves artifacts in place. The authors evaluated the behavior of this automated strategy using simulated datasets and a mock community with known ground truth, allowing them to assess how well the correction distinguished technical noise from real biology. They also used these benchmarks to measure the contribution of individual analytical components and the accuracy of taxonomic classification within the pipeline.

Downstream, the platform offers a suite of analyses that all operate within the same compositional framework. Diversity assessment uses compositionally appropriate metrics, and significance testing draws on established methods including PERMANOVA, PERMDISP, and ANOSIM, alongside principal coordinates analysis for visualization. Differential abundance analysis incorporates effect size estimation, and the platform supports approaches such as linear discriminant analysis effect size, robust centered log-ratio methods, and interquartile log-ratio transformations. Co-occurrence network inference and functional prediction, the latter covering both bacterial and fungal communities with reference to resources such as the Kyoto Encyclopedia of Genes and Genomes, round out the workflow. Because every step follows the same compositional logic, the results are internally consistent in a way that pieced-together pipelines often are not.

One of the platform’s more distinctive features is the split-CLR transformation, designed for cross-dataset analyses. When researchers want to combine datasets with different compositional structures, for example to run cross-kingdom correlation analyses linking bacteria and fungi, or paired-condition analyses within the same subjects, directly merging the data can introduce spurious correlations. Split-CLR addresses this by transforming each dataset appropriately before integration, reducing the artificial associations that arise from naive combination. This capability is particularly relevant to studies of polymicrobial diseases, where bacterial-fungal interactions may be clinically important but statistically treacherous to assess.

To demonstrate the platform in practice, the authors reanalyzed three published datasets spanning different study designs and sequencing technologies. A multi-cohort gout dataset allowed them to test batch correction across cohorts, and the analysis recovered biologically coherent patterns while highlighting additional findings supported by the literature, including distinctions related to hyperuricemia. A pediatric Crohn’s disease dataset covering both bacterial and fungal communities exercised the cross-kingdom analysis capabilities. Finally, a PacBio long-read periodontitis dataset confirmed compatibility with long-read data. Across all three, CoMeDA recovered patterns consistent with the original publications while surfacing additional literature-supported findings, suggesting the integrated workflow can add value without discarding established results.

Accessibility is a stated design goal. The platform features an interactive web interface and requires no programming expertise, lowering the barrier for clinical researchers and experimentalists who lack bioinformatics support. The open-source release, published under a Creative Commons Attribution 4.0 license, includes the Docker image for local deployment, an option likely to appeal to institutions handling sensitive clinical data. As microbiome studies grow in scale and increasingly pool cohorts across laboratories, tools that treat compositionality and batch structure as first-class citizens, rather than afterthoughts, may become essential infrastructure. CoMeDA’s authors position their platform as a step in that direction: a compositionally coherent workflow in which normalization, batch correction, and downstream analysis follow a single, consistent analytical logic from raw reads to biological interpretation.

Subject of Research: An integrated web platform for compositional analysis and batch correction of metabarcoding data

Article Title: CoMeDA: an integrated platform for compositional analysis of metabarcoding data with batch correction

Article References: Lee, C.-Y., & Chang, T.-H. (2026). CoMeDA: an integrated platform for compositional analysis of metabarcoding data with batch correction. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06705-8

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06705-8

Keywords: metabarcoding, microbiome, compositional data analysis, batch effect correction, 16S rRNA, ITS sequencing, CLR transformation, differential abundance, co-occurrence networks, cross-kingdom analysis, bioinformatics software, long-read sequencing

The study, led by Xiaolin Yang of Jamk University of Applied Sciences in Jyväskylä, drew on cross-sectional data from 1,014 participants, roughly 60 percent of them women, collected between 2018 and 2020. Each participant wore an accelerometer for seven consecutive days, allowing the researchers to capture not just how much people moved, but the texture of their days: how long they slept, how much time they spent sedentary at work versus during leisure, and how much light physical activity and moderate-to-vigorous physical activity accumulated in each context. That distinction matters, because an hour of sitting at a desk drafting a report is not physiologically or cognitively equivalent to an hour of sitting on a sofa, and an hour of lifting boxes taxes the body very differently from an hour of brisk walking.

Methodologically, the team went beyond the usual regression-on-minutes approach. Because the 24-hour day is a fixed budget, time spent in one behavior necessarily comes at the expense of another, and standard statistical models can distort the picture when the components of that budget are analyzed in isolation. The researchers therefore applied compositional data analysis, a framework that treats the full daily composition of sleep, sedentary time, light activity and moderate-to-vigorous activity as an interdependent whole, using isometric log-ratio transformations to model the relative geometry of the data. They then used isotemporal substitution models to ask a deceptively simple question: if a person shifted 60 minutes from one behavior to another, what would happen to their cognitive performance?

Cognition itself was measured with the Cambridge Neuropsychological Test Automated Battery, a computerized suite that yields scores across several domains: learning and memory, working memory, reaction time, and information processing, along with an overall composite. The analyses were adjusted for a battery of sociodemographic and health-related variables, and — crucially — stratified by sex and by occupational physical workload, splitting participants into sedentary workers, those in standing or moving occupations, and those in physically demanding jobs.

The results were striking for how context-dependent they proved. Among men, more time spent in light physical activity at work was associated with slower reaction times and weaker information processing. Among women, by contrast, more sedentary time at work was associated with better learning and memory. Neither pattern implies causation, and the effect sizes were small, but the direction of the associations is enough to make anyone who has ever assumed that workplace sitting is uniformly harmful pause for thought.

Occupational category reshaped the picture further. In sedentary workers, more occupational light physical activity was associated with lower overall cognitive performance, along with weaker learning and memory and slower information processing. In people whose jobs involved standing and moving, the story flipped: more leisure-time sedentary time was associated with better overall cognitive performance and better working memory, while more leisure-time light activity was associated with slower reaction times. And among those in physically demanding occupations, more sedentary time at work was associated with better learning and memory. In other words, the same behavior — sitting — appeared to carry different cognitive associations depending on whether it punctuated a physically taxing day or a desk-bound one.

The isotemporal substitution models added a practical dimension. Reallocating 60 minutes from occupational light physical activity into sedentary time or into moderate-to-vigorous physical activity was associated with modest cognitive benefits, particularly among men and among sedentary workers, with statistical significance at the p < 0.05 level. For a desk worker whose job involves long stretches of low-intensity puttering — walking between meetings, standing at copiers, light errands — the analysis suggests that replacing some of that low-grade occupational movement with either focused seated work or genuine vigorous exercise might be associated with sharper cognition. The authors emphasize that these associations were generally small, and the cross-sectional design means the arrow of causality could point either way: perhaps people with certain cognitive profiles gravitate toward certain kinds of work, rather than the work shaping the cognition.

That caveat is worth dwelling on, because it is where much of the scientific interest lies. One plausible interpretation is that occupational light activity often reflects jobs with high physical demands but low cognitive engagement, whereas sedentary work tends to coincide with cognitively stimulating tasks — reading, writing, problem-solving — that may themselves help maintain cognitive function in midlife. The cognitive benefit of leisure-time sitting among people in standing and moving occupations fits this reading: after a day on their feet, the sedentary hours of these workers may be filled with mentally engaging pursuits. Conversely, for workers whose days are already physically demanding, sedentary time at work may represent necessary recovery rather than idleness. The study cannot confirm these mechanisms, but the stratified patterns are consistent with the idea that the meaning of a behavior depends on the biological and cognitive context in which it occurs.

The sex differences add another layer. Why might occupational light activity correlate with slower reaction times in men while occupational sitting correlates with better memory in women? The authors do not offer a definitive answer, and the associations are modest, but the stratification itself is a methodological point worth celebrating. Many studies of movement behavior and cognition pool the sexes and blur occupational categories, potentially washing out real signals or producing misleading averages. By analyzing men and women, and sedentary and physically active occupations, separately, the Finnish team surfaced patterns that a single pooled model would likely have hidden.

For the millions of workers navigating the modern mix of desk time, chores and exercise, the takeaway is neither a license to sit all day nor a renewed guilt trip about the office chair. It is a call for nuance. The 24-hour day is a closed system, and the value of any given hour depends on what it replaces, where it happens, and who is spending it. The authors conclude that associations between movement behaviors and cognitive performance were generally small and context-dependent, varying by sex and occupational physical workload, and that the context in which movement behaviors are accumulated deserves far more attention in future research. As wearable accelerometers proliferate and longitudinal follow-ups of cohorts like the Young Finns study continue, the field may soon move from these cross-sectional snapshots to causal answers about which reallocations of our daily 24 hours genuinely protect the aging brain. Until then, the study stands as a reminder that in the physiology of everyday life, context is not a footnote — it is the finding.

Subject of Research: Associations between 24-hour movement behaviors and cognitive performance in middle-aged employees

Article Title: Compositional data analysis of 24-h movement behaviors and cognitive performance in middle-aged employees: the cardiovascular risk in Young Finns study

Article References: Yang, X., Kukko, T., Syväoja, H., Kulmala, J., Pahkala, K., Lehtimäki, T., Suominen, T. H., Raitakari, O. T., Rovio, S. P., & Tammelin, T. H. (2026). Compositional data analysis of 24-h movement behaviors and cognitive performance in middle-aged employees: the cardiovascular risk in Young Finns study. Journal of Activity, Sedentary and Sleep Behaviors. https://doi.org/10.1186/s44167-026-00117-y

Image Credits: AI Generated

DOI: 10.1186/s44167-026-00117-y

Keywords: movement behavior, cognitive performance, sedentary time, physical activity, compositional data analysis, isotemporal substitution, occupational health, sex differences, accelerometry, midlife cognition, Young Finns Study, working memory

The research, which forms part of Pham’s doctoral work at National Economics University in Hanoi, analyzes a balanced panel of 135 Vietnamese listed industrial firms covering the period from 2016 to 2024. That window is analytically valuable because it spans years of robust pre-pandemic expansion, the sharp disruption of COVID-19, and the uneven recovery that followed. By holding the sample constant across all nine years, the authors ensure that every firm contributes equally to the analysis, avoiding the selection biases that can creep in when companies enter or exit a dataset mid-study. The result is a comprehensive portrait of how the financial anatomy of Vietnam’s industrial listed sector has evolved through one of the most turbulent stretches in modern economic history.

What sets the study apart methodologically is its three-stage analytical framework, which begins with a technique rarely applied to corporate finance in emerging markets: compositional data analysis. Financial statements are inherently compositional in nature, because the components of a balance sheet or income statement are constrained to sum to a fixed total. Treating raw ratios without accounting for this constraint can produce misleading statistical artifacts, a problem long recognized in fields like geology and ecology but often ignored in financial research. By applying compositional data methods, the authors respect the true geometry of financial statement data, ensuring that the relative structure of assets, liabilities, and equity is analyzed in a mathematically coherent way.

Building on that foundation, the researchers employed Ward’s hierarchical clustering to sort firm-year observations into recurring financial profiles. Ward’s method is a clustering algorithm that merges observations in a way that minimizes the increase in within-group variance at each step, producing compact and internally coherent groups. Rather than forcing firms into arbitrary categories defined by regulators or rating agencies, the clustering lets the data speak for itself, revealing natural groupings based on the relative composition of each firm’s financial statements. The analysis identified four distinct financial profiles, ranging from financially robust firms to the most vulnerable companies whose balance sheets signal elevated structural risk.

The second stage of the framework tackles a persistent problem in performance research: the proliferation of accounting ratios. Analysts can compute dozens of measures covering profitability, leverage, liquidity, efficiency, and growth, but these indicators overlap heavily and often move together in ways that obscure rather than clarify a firm’s true condition. Pham and Dao addressed this by applying principal component analysis to eleven accounting ratios, distilling them into three composite indices that capture the dominant dimensions of variation across the sample. The first index summarizes overall Performance, the second captures Financial Risk, and the third measures Management Efficiency. Principal component analysis works by identifying linear combinations of the original variables that explain the maximum possible variance, effectively compressing a crowded dashboard of ratios into a small set of orthogonal summary scores that retain most of the informational content.

With firms classified into profiles and composite indices constructed, the third stage estimated the relationships between financial risk, management efficiency, and performance using interactive two-way fixed-effects regression models. Fixed-effects models control for time-invariant characteristics of each firm and for shocks common to all firms in a given year, which helps isolate the genuine association between the explanatory indices and performance from confounding factors such as managerial quality or industry-wide cycles. The models included interaction terms between the indices and profile membership, allowing the strength of each relationship to differ across the four financial profiles. Standard errors were clustered at the firm level to account for serial correlation in the residuals of individual companies over time.

The headline findings are both intuitive and consequential. Financial risk was negatively associated with performance across all four profiles, confirming that firms carrying heavier structural financial burdens tend to underperform regardless of their position on the risk spectrum. Crucially, however, the strength of this adverse relationship was not uniform. The most financially vulnerable profile exhibited the strongest negative association, suggesting that risk is not merely a drag on performance but an increasingly punishing one as a firm’s balance sheet deteriorates. This nonlinearity carries a clear implication for investors and policymakers: the marginal cost of additional leverage or liquidity strain is highest precisely where firms can least afford it.

Management efficiency told a different story. The composite index of efficiency was positively associated with performance in several of the identified profiles, and the relationship was significantly stronger in the largest profile of firms. In other words, operational discipline, asset utilization, and cost control appear to translate into superior performance most powerfully for the broad middle of Vietnam’s industrial listed sector, where competitive pressures reward managerial skill most directly. For the smallest and most vulnerable groups, the payoff to efficiency gains appears more muted, possibly because structural financial fragility overwhelms the benefits of good management, or because such firms face constraints that prevent efficient practices from being fully exploited.

Robustness testing is where the study earns particular credibility. The authors re-estimated their models using Driscoll-Kraay inference, a technique designed to handle cross-sectional dependence in panel data, where shocks to one firm may spill over to others through supply chains, credit markets, or common exposure to macroeconomic conditions. They also considered lagged profile membership, addressing the concern that a firm’s current performance might influence its profile classification rather than the other way around. The main patterns remained broadly consistent under both checks, indicating that the core conclusions are not artifacts of a particular statistical assumption or of simultaneity between classification and outcomes.

The broader significance of the research extends beyond Vietnam’s borders. Emerging markets often lack the deep institutional infrastructure of developed economies, making it harder for investors, creditors, and regulators to distinguish genuinely resilient firms from those that merely look healthy on headline metrics. A profile-based approach offers a complementary lens: instead of asking whether a firm beats a uniform sector-wide benchmark, analysts can ask whether a firm’s financial structure, risk exposure, and management efficiency combine in ways characteristic of resilient or fragile archetypes. As Vietnam continues to attract foreign investment and its industrial firms take on longer-term sustainability-oriented projects, the capacity to match assessment methods to financial profiles may prove essential for channeling capital toward the companies best positioned to sustain growth through the next decade of global economic uncertainty.

Subject of Research: Financial performance assessment of Vietnamese listed industrial firms using compositional data analysis and principal component analysis

Article Title: Evaluating the financial performance of Vietnamese listed industrial firms using compositional data analysis and PCA indexing

Article References: Pham, V. H., & Dao, T. T. B. (2026). Evaluating the financial performance of Vietnamese listed industrial firms using compositional data analysis and PCA indexing. Discover Sustainability. https://doi.org/10.1007/s43621-026-04976-y

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04976-y

Keywords: Vietnam, financial performance, compositional data analysis, principal component analysis, financial risk, management efficiency, hierarchical clustering, panel data, listed firms, industrial sector, corporate financial resilience, financial econometrics

The review, published in the Journal of Activity, Sedentary and Sleep Behaviors, was led by Denver M. Y. Brown of Kansas State University together with colleagues from institutions across North America. The team searched eight databases from their inception through late 2023, supplemented by forward and backward citation searches, a manual scan of the International Network of Time-Use Epidemiologists’ publication list, and direct consultation with experts. From 7,212 identified records, 102 observational studies met the strict inclusion criteria: each had to use CoDA with log-ratio transformations, capture all three movement behaviors across the full 24-hour day, and test associations with indicators of physical, cognitive, or mental health. The studies, published between 2016 and 2024, drew on samples from 29 countries, with analytic samples ranging from just 28 participants to more than 130,000, and participant ages spanning from infancy to late adulthood.

To make sense of such a heterogeneous literature, the researchers classified reporting practices into seven domains: methodological justification, behavioral measurement and data handling, composition construction, analytic plan, composition-specific descriptive statistics, model results, and auxiliary information. The extraction framework was reviewed by eleven international experts and drew on existing reporting checklists such as STROBE, designed for observational epidemiology, and CREMAS, developed for ecological momentary assessment studies. Each study was also scored with an adaptation of the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-sectional Studies, yielding a sobering baseline: 81 of the 102 studies were rated fair quality, 19 good, and two poor.

The brightest spot was methodological justification. Nearly every study, 100 of 102, mentioned CoDA in the title or abstract, and 94 explained why a compositional approach was appropriate for 24-hour time-use data. This is more than academic housekeeping. Because the 24-hour movement paradigm remains unfamiliar to many journal audiences, clear justification helps readers grasp why the co-dependency of behaviors demands special statistical treatment. It also aids discoverability: when authors flag CoDA prominently, systematic searches can actually find these studies, a prerequisite for any future synthesis of the evidence.

Beyond that strong opening, the picture fragments. In the measurement domain, most studies did describe how accelerometer data were scored and processed for each behavior, where devices were worn, how many minutes constituted a valid day, and how many valid days were required. Yet only 44 of the 90 device-based studies clearly stated which valid days entered the analysis, whether averages across all days, random selections, or weighted combinations of weekdays and weekends. That omission matters: recent work has shown that different data-handling strategies can materially change conclusions about how movement behaviors relate to obesity. Sleep fared worst of all. Only about half the studies clearly conceptualized what sleep they were measuring, whether nocturnal sleep, 24-hour sleep, or daytime naps, and fewer than 40 clearly defined the sleep variable itself. Only 17 studies explicitly included naps in the composition, and in 44 studies it was simply unclear. Without sleep logs, daytime sleep captured by accelerometers risks being misclassified as sedentary time, quietly corrupting the very compositions the analyses depend on.

Composition construction revealed similarly troubling gaps. All 102 studies reported how many parts the day was partitioned into, most commonly four: sleep, sedentary behavior, light physical activity, and moderate-to-vigorous physical activity, with compositions ranging from three parts up to nine. And 101 of 102 reported the log-ratio transformation used. But only six studies specified how a day was defined, midnight-to-midnight versus wake-to-wake, a detail that shapes what the composition actually represents. Most striking, only 27 studies addressed zeros in the behavioral data. Zeros are poison for log-ratio mathematics, since the logarithm of zero is undefined, and how researchers replace them can distort the entire analysis. Encouragingly, of the 19 studies that observed zeros, most used principled methods such as log-ratio expectation maximization imputation or multiplicative replacement, approaches recent methodological work has endorsed over crude substitution.

The analytic plan and results sections showed the deepest inconsistencies, and these are the ones with the greatest consequences for cumulative science. Nearly all studies reported their post-transformation analytic technique and whether covariates were adjusted, but fewer than a third compared their analytic sample to the full sample, and only 15 provided a power analysis, a gap the authors attribute largely to the field’s reliance on secondary data analysis. In the results, only 64 studies reported whether the overall 24-hour composition was significantly associated with the health outcome at all, and just 25 included a standardized effect size such as R-squared. From there, practices diverged into what the authors describe as selective reporting: some studies examined individual behaviors or ran isotemporal substitution models only when the overall model was significant, others ran them regardless, and some never stated whether the overall test was significant. Several studies reported associations for only some components of the composition, omitting exactly the kind of complete, per-behavior estimates that meta-analysis requires.

Isotemporal substitution, the technique that estimates what happens to a health outcome when minutes are swapped between behaviors, was the single most popular analytic approach, appearing in 84 studies. Here, reporting was somewhat better: 83 of the 84 studies clearly stated whether substitutions were one-to-one, such as ten minutes of sitting replaced by ten minutes of brisk walking, or proportional, where reallocated time is spread across all other behaviors. Most reported model statistics with confidence intervals or standard errors. Yet even here, roughly half only examined reallocations for outcomes already found significant, and 15 reported substitutions for only a subset of behaviors. Auxiliary reporting told a similar story of selective diligence: funding sources and conflicts of interest were almost universally disclosed, likely driven by journal requirements, but only 16 studies acknowledged the limitations of CoDA itself, and only 22 discussed whether their effect sizes were clinically meaningful, a crucial question when translating compositional proportions back into actionable minutes of activity.

The stakes of this inconsistency extend well beyond academic tidiness. The 24-hour movement paradigm underpins integrated guidelines now adopted across multiple countries for children, adults, and older adults, and it increasingly informs public health policy about how we should structure our days. If individual studies cannot be compared, and if selective reporting blocks the extraction of complete effect estimates, the field cannot perform the meta-analyses needed to establish robust, population-level conclusions about the optimal distribution of sleep, sitting, and movement. The review’s authors point to a proven path forward: the CREMAS checklist for ecological momentary assessment was born from exactly this kind of systematic characterization of existing practices, and they propose a similar route for CoDA, using Delphi methodology, an iterative consensus process among experts and end-users, to develop a formal reporting checklist for observational compositional studies. They also caution that their conservative approach, counting unmentioned items as unreported, may underestimate actual practice, and that emerging techniques will require the standards to evolve. Still, the message is unambiguous. A field that has grown from two studies in a 2020 systematic review to more than a hundred in under a decade now needs to grow up statistically: consistent, transparent, and complete reporting is the price of turning a promising method into trustworthy evidence about how the whole 24-hour day shapes human health.

Subject of Research: Reporting practices in observational studies using compositional data analysis to examine associations between 24-hour movement behaviors and health indicators

Article Title: A systematic review of research reporting practices in observational studies examining associations between 24-h movement behaviors and indicators of health using compositional data analysis

Article References: Brown, D. M. Y., Burkart, S., Groves, C. I., Balbim, G. M., Pfledderer, C. D., Porter, C. D., Laurent, C. S., Johnson, E. K., & Kracht, C. L. (2024). A systematic review of research reporting practices in observational studies examining associations between 24-h movement behaviors and indicators of health using compositional data analysis. Journal of Activity, Sedentary and Sleep Behaviors, 3(1), Article 23. https://doi.org/10.1186/s44167-024-00062-8

Image Credits: AI Generated

DOI: 10.1186/s44167-024-00062-8

Keywords: compositional data analysis, 24-hour movement behaviors, physical activity, sedentary behavior, sleep, systematic review, reporting practices, time-use epidemiology, isotemporal substitution, accelerometry, research methods, public health

The concept at the heart of this review is deceptively simple. Physical activity, sedentary behavior and sleep are known collectively as the 24-hour movement behaviors, and they possess two defining properties. First, they are mutually exclusive: at any given moment, a person is doing only one of them. Second, they are reciprocal: adding time to one behavior necessarily subtracts it from the others. A child who runs around all afternoon may fall asleep faster and sleep longer that night, illustrating how the behaviors influence one another across the day. Recognizing this interdependence, a Canadian research group published the first integrated 24-hour movement guidelines for children and youth in 2016, and similar guidance for the early years, adolescents, adults and older adults has since been adopted by high-income and low- and middle-income countries alike. In 2020, the World Health Organization released its own integrative guidelines for physical activity and sedentary behavior, cementing the shift from single-behavior thinking to whole-day thinking.

Two events in that same year turbocharged the field. The first was the WHO guideline release itself; the second was the COVID-19 pandemic, which disrupted daily routines worldwide. Systematic reviews documented that during lockdowns many people became less active, sat more, and experienced altered sleep duration and timing, changes linked to weight gain and impaired mental health, especially in children. The pandemic also acted as an accidental accelerant for research: publications on 24-hour movement behaviors numbered just 51 before 2020, then tripled to 150 within 2020 and 2021 alone. It is against this backdrop of explosive growth that the team, led by Chelsea Kracht of the University of Kansas Medical Center, set out to map the terrain. Their umbrella review, registered in advance and reported according to PRISMA-ScR guidelines, searched eight databases from inception through late 2023, supplemented by forward and backward citation searching, expert consultation, gray literature and a dedicated time-use epidemiology database.

After screening more than 1,800 references and resolving duplicates, the researchers identified 32 eligible reviews: 20 systematic reviews, 10 meta-analyses and 2 pooled analyses. These reviews collectively included a median of 26 primary studies each, with a range of 5 to 141, and spanned populations from infants to older adults, including immigrant children, children in Arabic-speaking countries, college students, airline pilots and people living with type 1 diabetes. All 32 reviews assessed physical activity, and 26 of them covered all three behaviors. Most were published between 2020 and 2023, and the studies they synthesized came predominantly from high-income Western countries, East and Southeast Asia, and Latin America and the Caribbean. The health outcomes examined skewed heavily toward physical health, with weight, cardiometabolic markers and fitness assessed in at least 11 of the 26 outcome-focused reviews, while mental health received comparatively less attention despite mounting concern about youth psychological wellbeing.

The review’s second aim tackled a question of enormous practical importance: how many people actually meet the 24-hour movement guidelines? The answer, at least for children and adolescents, is alarmingly few. Drawing on seven reviews that reported prevalence estimates, the authors found that roughly half of children met the physical activity guideline, with estimates ranging from 22.3 to 67 percent. Fewer than a third met the screen-time guideline, with estimates between 10 and 28.3 percent. Sleep fared best, with 57 to 83.5 percent meeting duration recommendations. But the composite picture was stark: only 3.5 to 12.8 percent of children met all three guidelines simultaneously, while 7 to 28.6 percent met none at all. Adherence declined steadily from preschool to adolescence, and pandemic-era estimates dipped slightly lower still. The authors caution that the meta-analyses behind these figures showed very high heterogeneity, with I-squared values exceeding 95 percent, meaning the underlying studies differed substantially. Adult prevalence data were scarce, limited to two reviews covering airline pilots and UK adults, which reported insufficient activity in roughly 21 to 52 percent of adults depending on the metric used.

The third and most technically ambitious aim examined how researchers analyze the relationships between these behaviors and health. Because the three behaviors are collinear, ordinary regression on absolute minutes can be misleading, so the field has embraced two specialized approaches. Isotemporal substitution modeling, or ISM, estimates what happens to a health outcome when a fixed block of time is hypothetically reallocated from one behavior to another, for instance swapping 30 minutes of sitting for 30 minutes of brisk walking. Compositional data analysis, or CoDA, goes further by transforming behavioral data through log ratios, treating each behavior as a relative component of the fixed 24-hour day rather than an independent quantity. CoDA, first applied to movement behaviors in 2015 and central to the VIRTUE framework for time-use epidemiology, allows researchers to explore the total composition, combined parts and individual behaviors in relation to health. Reviews also used simpler threshold-based guideline adherence and profile-based groupings, such as comparing children with high activity and low sleep against other combinations.

Across this methodological diversity, one signal emerged with remarkable consistency: moderate-to-vigorous physical activity is the powerhouse of the 24-hour day. Higher amounts of MVPA were favorably associated with child weight outcomes, child fitness, motor skills in young children, adult quality of life, depression in children and adolescents, and all-cause mortality. The ISM reviews reinforced this, showing that reallocating sedentary time to MVPA predicted favorable changes in weight, cardiometabolic health, fitness and mortality, while swapping sedentary time for light-intensity activity yielded weaker or inconsistent benefits. Meeting all three guidelines was likewise associated with favorable weight outcomes, cardiometabolic markers, fitness, cognition and mental health in children and adolescents, though results were less consistent in toddlers and preschoolers. One pooled analysis of accelerometer data found that the ratio of light activity to sedentary time predicted mortality in hip-worn devices but not wrist-worn ones, hinting at how measurement choices can shape conclusions. Intriguingly, high physical activity appeared to blunt some of the mortality risk associated with short sleep.

The picture for the other two behaviors was far murkier. Sedentary behavior showed unfavorable associations with child fitness, but null or mixed results for adult cardiometabolic markers, and its effects appeared to depend on how it was measured and how much MVPA a person accumulated. Sleep findings were similarly mixed: better sleep quality or duration was linked to improved depression and anxiety in people with type 1 diabetes and reduced fatigue in college students, yet associations with child weight and glycemic control were null or inconsistent, and hypothetically substituting sleep time with other behaviors generally showed no effect on weight, cardiometabolic health, fitness or mortality. Part of the confusion, the authors argue, stems from sloppy definitions. Fewer than a third of reviews clearly defined the behaviors they studied, many never specified whether screen-time counted as sedentary behavior, and agreed-upon terminology standards published nearly a decade ago went largely ignored. Sleep, which can be characterized by duration, quality, timing and more, was usually reduced to duration alone.

Compounding these definitional problems is a quality crisis. Using the AMSTAR 2 appraisal tool, the reviewers rated only two of the 30 gradable reviews as moderate quality; seven were rated low and 21 were rated critically low. Few reviews listed excluded studies with reasons, only four of twelve meta-analyses assessed publication bias, none reported funding sources for their included studies, and all six reviews that applied the GRADE framework judged the evidence for their health outcomes to be low or very low quality, largely due to serious risk of bias and inconsistency. The authors outline a four-part path forward: prioritize longitudinal multi-behavior studies, develop consensus reporting standards for approaches like CoDA, extend reviews to underrepresented populations including people with disabilities and pregnant or postpartum individuals, and hold future syntheses to rigorous existing standards. As the 24-hour movement guidelines approach their first decade, the message for readers is clear and actionable. The strongest evidence supports getting plenty of moderate-to-vigorous exercise and aiming for healthy amounts of all three behaviors every day, because in the economy of the 24-hour day, every minute genuinely counts.

Subject of Research: Adherence to 24-hour movement behavior guidelines for physical activity, sedentary behavior and sleep and their associations with health outcomes

Article Title: 24-hour movement behavior adherence and associations with health outcomes: an umbrella review

Article References: Kracht, C. L., Burkart, S., Groves, C. I., Balbim, G. M., Pfledderer, C. D., Porter, C. D., St. Laurent, C. W., Johnson, E. K., & Brown, D. M. Y. (2024). 24-hour movement behavior adherence and associations with health outcomes: an umbrella review. Journal of Activity, Sedentary and Sleep Behaviors, 3(1), Article 25. https://doi.org/10.1186/s44167-024-00064-6

Image Credits: AI Generated

DOI: 10.1186/s44167-024-00064-6

Keywords: 24-hour movement behaviors, physical activity, sedentary behavior, sleep, umbrella review, guideline adherence, compositional data analysis, isotemporal substitution, public health, mental health, children and adolescents, systematic review quality

The scientific motivation behind the study stems from a fundamental shift in how researchers think about physical activity, sedentary behaviour, and sleep. For decades, epidemiologists examined each of these behaviours in isolation, asking, for example, whether more exercise lowers the risk of heart disease or whether prolonged sitting raises the risk of type 2 diabetes. The evidence from such studies is clear: higher physical activity, less sedentary time, and adequate sleep are each associated with lower risks of cardiovascular disease, type 2 diabetes, obesity, several cancers, anxiety, depression, and premature mortality. But there is a hidden flaw in treating these behaviours separately. Time is finite. Every additional minute spent sitting is a minute not spent standing, stepping, or sleeping. The three behaviours are co-dependent components of a single twenty-four-hour whole, and analysing them independently distorts their true relationships with health.

This realisation has driven what researchers call the 24-hour movement paradigm, a framework that treats physical activity, sedentary behaviour, and sleep as inseparable parts of a daily time-use composition. The paradigm has already reshaped public health policy: several countries, including Canada, Australia, Thailand, South Africa, Saudi Arabia, and Egypt, along with the World Health Organization, have issued integrated 24-hour movement guidelines. Yet the first generation of these guidelines was built largely on studies that examined single behaviours in isolation, because rigorous evidence on their combined effects simply did not exist. The authors of the Canadian 24-hour movement guidelines for adults explicitly called for research with stronger designs, including longitudinal follow-up, device-measured behaviours, and attention to dimensions such as domain-specific activity, sleep consistency, napping, and daytime alertness. The Slovenian team designed the GIB study to answer exactly these calls.

The recruitment strategy leverages an existing national infrastructure. Every four years, the Statistical Office of Slovenia draws a nationally representative sample of 17,500 residents aged 18 to 74 from the Central Population Register, using stratified simple random sampling with region and settlement type as strata. These individuals are invited by post to complete the National Health-related Lifestyle Survey, conducted by the National Institute of Public Health, which historically achieves a response rate of around 60 percent. From the roughly 10,500 survey responders, the GIB team expects about 5 percent to accept an additional invitation to the device-based study, yielding approximately 525 participants per recruitment wave. The first baseline data collection took place in 2024, with subsequent waves planned for 2028, 2032, and beyond, creating a rolling cohort intended to span more than a decade.

At the heart of the measurement strategy sits the activPAL4micro, a small tri-axial accelerometer measuring just 4 by 2 by 0.4 centimetres and weighing 9 grams, which is attached to the front of the thigh. Unlike wrist-worn devices, thigh placement allows the monitor to distinguish posture with high precision: sitting, standing, lying, and stepping can be classified directly from body position rather than inferred from wrist movement. Participants wear the device for seven consecutive days, twenty-four hours per day, including during sleep, and simultaneously keep a diary recording bedtimes, wake times, naps, night-time awakenings, and, for employed participants, working hours. Sleep onset and offset are then determined by combining the algorithmic output with diary entries, with adjustments applied when the device records upright activity near the reported sleep window. The result is a complete, posture-based map of each participant’s entire week.

From these raw data, the researchers will construct two complementary time-use compositions. The first is posture-based, comprising stepping, standing, sitting, lying while awake, and sleeping. The second is movement-based, comprising moderate-to-vigorous physical activity, light-intensity physical activity, sedentary behaviour, and sleep. For employed participants, a third, domain-specific composition will separate occupational from non-occupational behaviour, allowing the team to ask whether physical activity at work carries the same health value as leisure-time exercise, a question with major implications for millions of workers in physically demanding jobs. Non-wear time will be proportionally reallocated to wake-time behaviours, daytime naps will be added to sleep time, and validated algorithms will process the event files in the R programming environment.

The device data will be complemented by an extensive battery of questionnaires. A novel ten-item instrument, the GIB24-Q, developed specifically for Slovenian population surveillance, asks about domain-specific moderate-to-vigorous activity, muscle-strengthening and balance exercise, walking, sedentary behaviour, recreational screen time, and sleep. The widely validated Global Physical Activity Questionnaire provides an international benchmark. The 46-item Healthy Built Environment Questionnaire captures perceptions of neighbourhood, home, and workplace environments, along with time spent outdoors and in nature. Each participant’s home address will also be linked to the Walk Score index, an open-access measure of neighbourhood walkability ranging from 0 to 100, and to environmental indicators including air quality and the number of days with high temperatures, enabling the team to test whether the places people live shape the way they move.

Well-being and health will be assessed through eight short validated instruments: the WHO-5 Well-being Index for subjective mental well-being, the Need for Recovery Scale for work-related fatigue, the International Fitness Scale for perceived fitness, the Epworth Sleepiness Scale for daytime sleepiness, the Fatigue Assessment Scale for chronic fatigue symptoms, the Satisfaction With Life Scale, the Perceived Time Poverty Scale, which measures the feeling of lacking freely disposable time, and the Nordic Musculoskeletal Questionnaire for pain across nine body regions. In parallel, each participant’s data will be linked to the National Health-related Lifestyle Survey, which covers demographics, diet, smoking, alcohol, self-rated health, diagnosed conditions, medication use, stress, social support, body mass index, and waist circumference. From 2028 onward, with additional informed consent, the team will link records to national registers covering healthcare utilisation, hospitalisations, drug prescriptions, sickness absence, retirement, disease incidence, and causes of death, all under strict data protection law and GDPR compliance.

The statistical approach is as innovative as the data collection. Because the components of a 24-hour composition are co-dependent, conventional regression is inappropriate; the team will instead apply compositional data analysis, expressing time-use as sets of isometric log-ratio coordinates before entering regression models, handling zeros with a log-ratio expectation-maximisation algorithm, and applying survey weights to preserve national representativeness. An a priori power analysis determined that the expected sample of roughly 472 participants per wave providing valid accelerometer data is sufficient to detect a minimum effect size of 0.2 with 80 percent power. For significant findings, the researchers will map what they call the Goldilocks Zone: the range of 24-hour movement compositions, estimated at ten-minute granularity, associated with the best 5 percent of predicted health outcomes, and will search for compositions that fall in the optimal zone across multiple health outcomes simultaneously.

The potential payoff is considerable. The GIB study will deliver the first device-measured estimates of 24-hour movement behaviours in a national sample of Slovenian adults, identify the socio-demographic and environmental factors that shape healthy and unhealthy time use, and clarify the relative importance of occupational versus leisure activity, sleep regularity, and specific postures for long-term health. As Slovenia develops its own 24-hour movement guidelines, the findings will provide directly relevant, population-specific evidence, and the team anticipates contributing data to international consortia pooling thigh-worn accelerometer records worldwide. If the study succeeds in defining Goldilocks Zones for daily time use, the humble question of how a nation spends its twenty-four hours could yield some of the most actionable health guidance of the coming decade.

Subject of Research: A longitudinal population-based study of 24-hour movement behaviours, their correlates, and health associations in Slovenian adults

Article Title: Prevalence, correlates, and health associations of 24-hour movement behaviours in Slovenian adults: a protocol for a longitudinal population-based study

Article References: Kastelic, K., Knific, T., & Šarabon, N. (2025). Prevalence, correlates, and health associations of 24-hour movement behaviours in Slovenian adults: a protocol for a longitudinal population-based study. Journal of Activity, Sedentary and Sleep Behaviors, 4(1), Article 3. https://doi.org/10.1186/s44167-025-00073-z

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DOI: 10.1186/s44167-025-00073-z

Keywords: 24-hour movement behaviours, physical activity, sedentary behaviour, sleep, compositional data analysis, accelerometry, longitudinal study, population health, built environment, well-being, Slovenia, movement guidelines

The review, led by Nicholas Kuzik of the Healthy Active Living and Obesity Research Group at the Children’s Hospital of Eastern Ontario Research Institute, together with Markus J. Duncan, Natalie Beshara, Matthew MacDonald, Diego Augusto Santos Silva and Mark S. Tremblay, examined studies applying compositional data analysis, or CoDA, to 24-hour movement behaviours in school-aged children aged 5 to 17.9 years. Unlike conventional analyses that treat sleep, sedentary time, light physical activity and moderate to vigorous physical activity as independent variables, CoDA respects the zero-sum reality of the day. It treats time-use data as relative information, typically through isometric log-ratio transformations, so that the effect of any single behaviour is always interpreted in the context of all the others. Since Chastin and colleagues first applied the technique to movement behaviours in 2015, the field has grown rapidly, and this review is the first attempt to pool its results quantitatively across health domains.

The team registered the review with PROSPERO and followed PRISMA guidelines, searching MEDLINE, EMBASE, PsycINFO, CINAHL and SPORTDiscus up to May 24, 2024, and restricting inclusion to peer-reviewed English-language studies published since January 2014. From 1,217 identified records, 26 studies survived full-text screening. Sample sizes ranged from 88 to 5,828 participants with a median of 387, and mean ages spanned 8.0 to 16.4 years. Twenty-three studies were cross-sectional, two reported both cross-sectional and longitudinal results, and one was purely longitudinal. Study quality, appraised with the Joanna Briggs Institute checklist, was high for most groupings, with only four categories rated moderate.

The 26 studies reported their compositional results in four distinct ways. Sixteen used regression parameters, typically pivot coordinates that isolate the influence of one behaviour relative to the rest of the composition. Twelve reported substitution effects, estimating what happens when a fixed slice of time is swapped from one behaviour to another. Three identified optimal compositions, the so-called Goldilocks Days, and one compared clusters of movement behaviours. Because reporting practices varied so widely, the authors standardized substitution effects to 10-minute increments and converted them to standardized mean differences, pooling them with random-effects models that anticipated substantial heterogeneity across the many health outcomes involved.

The descriptive picture that emerged is itself a public health warning. Across the 51 samples contributing data, the weighted compositional means were approximately 9.1 hours of sleep, 8.9 hours of sedentary behaviour, 5.1 hours of light physical activity and just 0.8 hours, about 49.8 minutes, of moderate to vigorous physical activity per day. That figure sits below the 60-minute daily recommendation, a shortfall echoed globally: the Global Matrix 4.0 report card across 57 countries graded children a D for overall physical activity, with only 27 to 33 percent meeting the guideline, and a D-plus for sedentary behaviour, with 34 to 39 percent keeping recreational screen time under two hours.

The pooled regression results were largely null, but telling patterns surfaced within specific outcome categories. Every one of the ten associations between moderate to vigorous physical activity and adiposity indicators was favourable, as were nine of eleven associations with cardiometabolic biomarkers. Light physical activity, by contrast, was unfavourable in eight of ten adiposity findings and five of seven cognitive findings, while sedentary behaviour was unfavourable in seven of ten adiposity results. In other words, when the whole day is held constant, the intensity of movement matters: the modest minutes children spend truly moving appear to do disproportionate work, while the long hours of gentle activity and sitting lean the other way.

The meta-analyses of substitutions delivered the review’s most striking quantitative message. Replacing 10 minutes of sedentary behaviour, sleep or light activity with 10 minutes of moderate to vigorous physical activity was associated with a 0.1 standardized mean difference improvement in overall health and well-being. The reverse trade was far more damaging: surrendering 10 minutes of that vigorous activity to any of the other three behaviours produced a 0.2 standardized mean difference decline. As the authors put it, the risk of losing vigorous activity is double the magnitude of the benefit of gaining it. One illustrative study found that middle school students who exchanged 10 minutes of sleep or sitting for vigorous movement saw a 0.1 drop in BMI z-score. The practical implication is urgent: beyond promoting more exercise, public health strategy must actively protect the limited vigorous activity children already accumulate, since once lost, it is disproportionately hard to win back.

Longitudinal substitutions, drawn from only two studies, hinted that sleep’s benefits may grow over time, with a 10-minute swap of sleep for vigorous activity associated with a small unfavourable change while the sedentary-for-vigorous swap remained favourable, though effect sizes were tiny and the evidence preliminary. The Goldilocks Day analyses, meanwhile, offered a glimpse of what an optimized day might look like. Across two studies of adiposity, the weighted ideal comprised 9.8 hours of sleep, 10.1 hours of sedentary behaviour, 2.5 hours of light activity and 1.6 hours of moderate to vigorous physical activity. For broader health spanning mental, cognitive and physical domains, one study pointed to 10.4 hours of sleep, 9.7 hours of sedentary time, 2.4 hours of light activity and 1.5 hours of vigorous activity. Yet even here, methodological fragility loomed large: two analyses of the same bone health dataset diverged by more than an hour in their recommended vigorous activity simply because one weighted accelerometer data to account for weekends and the other did not.

That sensitivity underscores the review’s methodological critique. Only nine of the 26 studies reported dispersion metrics for movement behaviours, leaving uncertainty about how much children’s time use actually varies. Most studies reported only the first pivot coordinate rather than full model parameters, limiting replication and meta-analysis. And the handling of accelerometer non-wear time emerged as a hidden elephant in the room: normalizing geometric means to 1,440 minutes implicitly imputes unmeasured time in the same ratios as measured time, which can push a child recording 7 hours of sleep and 45 minutes of vigorous activity into apparent compliance with both guidelines. The authors argue for epoch-by-epoch imputation techniques, consensus on accelerometer processing, a DELPHI-derived reporting checklist for observational CoDA studies, and routine reporting of arithmetic means alongside geometric ones.

The review’s limitations are acknowledged candidly: heterogeneity across outcomes forced broad category groupings, the a priori questions about optimal distributions and covariates could not be fully answered with available data, and the substitution effects depend on baseline compositions that varied between studies, sometimes by more than the 10-minute increments being modelled. Still, the core conclusions align with earlier reviews, including a 2025 meta-analysis of adiposity outcomes, while adding a sharper edge. The messages of moving more, sitting less and sleeping well survive the compositional lens, but the day’s arithmetic gives them new weight. In a world where the average child logs under 50 minutes of vigorous activity, every one of those minutes is an asset whose loss costs twice what its gain delivers, and the field’s next task is to move from more-or-less messaging toward the precise, personalized balances that a constrained 24-hour day demands.

Subject of Research: Compositional data analysis of 24-hour movement behaviours and health outcomes in school-aged children

Article Title: A systematic review and meta-analysis of the first decade of compositional data analyses of 24-hour movement behaviours, health, and well-being in school-aged children

Article References: Kuzik, N., Duncan, M. J., Beshara, N., MacDonald, M., Silva, D. A. S., & Tremblay, M. S. (2025). A systematic review and meta-analysis of the first decade of compositional data analyses of 24-hour movement behaviours, health, and well-being in school-aged children. Journal of Activity, Sedentary and Sleep Behaviors, 4(1), Article 4. https://doi.org/10.1186/s44167-025-00076-w

Image Credits: AI Generated

DOI: 10.1186/s44167-025-00076-w

Keywords: compositional data analysis, 24-hour movement behaviours, physical activity, sedentary behaviour, sleep, children, adolescents, meta-analysis, systematic review, MVPA, adiposity, health guidelines

The study, published in the Journal of Activity, Sedentary and Sleep Behaviors, was led by Aaron Miatke and colleagues at the University of South Australia as part of the Life on Holidays project, a longitudinal cohort study that followed children from twenty-four primary schools in Adelaide between February 2019 and December 2022. The researchers recruited 381 children in Grade 4, drawn from neighbourhoods spanning low, middle and high socio-economic strata, and equipped them with wrist-worn GENEActiv accelerometers that recorded movement around the clock for seven consecutive days at each of five measurement timepoints. Unlike conventional studies that measure only waking hours, this design captured the full daily cycle of sleep, sedentary behaviour, light activity and moderate-to-vigorous activity, allowing the team to model the entire twenty-four-hour day as a single, interconnected system.

That modelling approach, known as compositional data analysis, or CoDA, is what gives the study its statistical teeth. The central insight of CoDA is deceptively simple: a day is a fixed budget of twenty-four hours, so any increase in one behaviour must come at the expense of another. Traditional statistics that treat sleep, sitting and exercise as independent variables ignore this zero-sum reality and can produce misleading conclusions. To handle the problem, the researchers transformed their four-part time-use composition into isometric log-ratio coordinates, a mathematical technique that expresses each behaviour relative to the geometric mean of the others. They then built linear mixed-effects models that accounted for the clustering of children within schools, repeated measurements within children, and covariates including sex, age and socio-economic status. Crucially, they ran the analysis twice: once as a cross-sectional snapshot at baseline, and once longitudinally, using the actual annualised rates of change in body fat, body mass index and cardio-respiratory fitness across three distinct periods, the first school year, the intervening summer holidays, and the second school year.

The cross-sectional results looked reassuringly familiar. At baseline, children who spent more time in moderate-to-vigorous physical activity had lower body mass index z-scores, lower percentages of body fat measured by bioelectrical impedance, and higher estimated maximal oxygen uptake from the twenty-metre shuttle run test, all statistically significant at p less than 0.01. Time spent in light physical activity showed the opposite pattern, unfavourably associated with every outcome, while sedentary behaviour was linked to higher body fat and longer sleep to lower body fat. In other words, the snapshot data replicated the picture painted by most previous research: the whole menu of daily behaviours appeared to matter.

Then came the twist. When the researchers switched to the longitudinal models, which tracked empirical changes in each child’s body over two years rather than comparing children to one another at a single moment, almost all of those associations evaporated. Sleep, sedentary behaviour and light activity lost their statistical significance entirely. Only moderate-to-vigorous physical activity remained a significant predictor of change, and even its association with body fat percentage only approached conventional significance at p equal to 0.06 before the sensitivity analyses sharpened the picture. The overall time-use composition was significantly associated with changes in cardio-respiratory fitness, and the composition explained roughly six percent of the variance in fitness change, but a mere 0.3 percent of the variance in body mass index change and two percent in body fat change.

The isotemporal substitution models, which estimate what would happen if a child traded minutes of one behaviour for another while holding everything else constant, quantified the stakes. Adding thirty minutes per day of moderate-to-vigorous activity at the expense of light activity was associated in cross-sectional models with a reduction in body fat percentage of 2.65 points and a gain of 1.23 millilitres per kilogram per minute in estimated VO2max. In the longitudinal models, the same thirty-minute reallocation from light activity was linked to a faster decline in body fat percentage, a rate of change 0.60 percentage points more favourable per year, and to a VO2max trajectory 0.91 units higher, with similar gains whether the traded-away time came from sleep, sitting or light movement. Notably, the asymmetry ran both ways: the researchers found that the detrimental association of losing moderate-to-vigorous activity was consistently larger than the benefit of gaining it, a warning that declines in activity, such as those seen during weekends, holidays and the transition into adolescence, may exact a disproportionate toll.

Perhaps the most striking discovery came from the sensitivity analyses, which split the moderate-to-vigorous category into its moderate and vigorous components. When vigorous physical activity stood alone, it was significantly and favourably associated with changes in both body fat percentage and VO2max over time, while moderate activity alone lost significance for every outcome. The dose-response comparison was dramatic: just ten minutes per day of additional vigorous activity, swapped for sedentary time, produced an estimated improvement in fitness trajectory of 0.53 units, identical to the improvement produced by thirty minutes per day of additional moderate activity. On a minute-for-minute basis, vigorous exercise was roughly three times as potent for cardio-respiratory fitness. For body fat, only reallocations toward vigorous activity, not moderate activity, reached significance in the longitudinal models.

The authors are careful about interpretation. They suggest that lower-intensity behaviours may simply not provide sufficient physiological stimulus to drive measurable change, whereas moderate-to-vigorous activity carries higher energy expenditure and greater cardiorespiratory demand, provoking the adaptations that improve fitness and body composition. They also note that cardio-respiratory fitness may respond faster to activity changes than adiposity, which is heavily influenced by diet, and that body mass index, which cannot distinguish fat from muscle and bone, may be a blunt instrument in growing children. The study has limitations worth acknowledging: the COVID-19 pandemic disrupted data collection, children from lower socio-economic areas were under-represented among completers, diet and pubertal development were not directly measured, and no winter data were collected, leaving seasonal effects unexplored.

For a world grappling with more than 340 million children affected by obesity and a generation whose fitness is measurably lower than their parents’, the message is both sobering and actionable. Simply cutting screen time or encouraging gentle movement, the study suggests, is unlikely to move the needle on its own. The real leverage lies in getting children breathing hard, whether through sport, sprinting games or active play, and in protecting the vigorous activity they already do. Even ten minutes a day of genuine huff-and-puff exertion, the data indicate, may be worth more than half an hour of anything gentler, a finding that could reshape physical activity guidelines and school programmes alike.

Subject of Research: Associations between 24-hour time use and adiposity and cardio-respiratory fitness in school-aged children

Article Title: Cross-sectional and longitudinal associations between sleep, sedentary behaviour and physical activity with adiposity and cardio-respiratory fitness in school-aged children: a compositional data analysis

Article References: Miatke, A., Olds, T., Maher, C., Fraysse, F., & Dumuid, D. (2025). Cross-sectional and longitudinal associations between sleep, sedentary behaviour and physical activity with adiposity and cardio-respiratory fitness in school-aged children: a compositional data analysis. Journal of Activity, Sedentary and Sleep Behaviors, 4(1), Article 11. https://doi.org/10.1186/s44167-025-00082-y

Image Credits: AI Generated

DOI: 10.1186/s44167-025-00082-y

Keywords: physical activity, children's health, compositional data analysis, adiposity, cardio-respiratory fitness, MVPA, vigorous exercise, sedentary behaviour, sleep, accelerometry, childhood obesity, longitudinal study