The authors begin from an uncomfortable observation: decades of behavior change research, from the theory of planned behavior to self-determination theory and socioecological models, explain only a modest proportion of the differences in physical behavior between and within individuals. People are notoriously inconsistent—one week they jog daily, the next they barely leave the sofa. The researchers attribute this weak predictive power to three structural shortcomings in existing models. First, most frameworks focus either on the short-term determinants that drive behavior or on the long-term health consequences that flow from it, rarely both. Second, they overlook the fact that consequences can loop back and become determinants: fitness gained through months of exercise also shapes how active you feel like being tomorrow. Third, and most fundamentally, they ignore the arithmetic of the day itself.

That arithmetic is unforgiving. Sleep, sedentary behavior, and physical activity are locked together within the fixed 1,440 minutes available, so every additional minute spent in one behavior must come at the expense of another. A person who adds thirty minutes of morning running necessarily removes thirty minutes from sleep, sitting, or some other activity. Traditional single-behavior studies, which examine physical activity in isolation, cannot capture these trade-offs. The new model builds on earlier 24-hour frameworks—including Pedišić’s Activity Balance Model, the Framework for Viable Integrative Research in Time-Use Epidemiology, and Rosenberger and colleagues’ 24-hour Activity Cycle—but goes further by explicitly embedding the full daily composition within a dual-process psychological architecture and a health-outcome architecture, connected by eight distinct pathways labeled A through H.

The left side of the model, adapted from the neurocognitive affect-related model, describes how the brain’s moment-to-moment machinery shapes what we do with our time. Its central constructs are affective states and executive functions. Following the work of Duncan and Barrett, the authors treat affect as a neurophysiological state defined by two dimensions: valence, the pleasantness or unpleasantness of a feeling, and energetic arousal, the level of activation. Executive functions, as defined by Diamond, encompass inhibition control, working memory, and cognitive flexibility—the mental capacities that let us plan, resist temptation, and stay focused on long-term goals despite distractions. The model proposes that optimal daily compositions of sleep, sitting, and activity can enhance these executive functions, which in turn foster more positive affective responses, which then facilitate healthier future behavior compositions.

Crucially, these pathways are reciprocal. Pathway A suggests that executive-function-based cognition shapes the emotional responses induced by exercise or prolonged sitting; pathway B connects those emotional responses to the future balance of daily behaviors, since affective reactions during a workout predict whether a person returns to exercise. Pathways C and D close the loop: cognitive preparation such as time management and goal setting influences the day’s activity composition, while the composition itself—regular exercise, adequate sleep—feeds back to improve cognitive function. The authors also acknowledge bidirectionality in the reverse direction, noting that physical behavior can predict momentary affect and that momentary affect can predict executive function performance, making the entire left side of the model a dynamic, self-modifying system rather than a one-way causal chain.

The right side of the model, adapted from the health model of Bouchard, Blair, and Haskell, traces the physiological consequences. Here the claim is that an optimal daily composition improves health-related fitness markers such as cardiorespiratory fitness (pathway E), which then improves the state of physical and mental health, from well-being to mortality risk (pathway F). The evidence base for these links is substantial. A systematic review of fifty-six isotemporal substitution studies concluded that reallocating time between sleep, sedentary behavior, light-to-moderate activity, and moderate-to-vigorous activity is associated with numerous health outcomes. A meta-analysis further showed that short sleep is significantly linked to mortality, diabetes, cardiovascular disease, coronary heart disease, and obesity. These pathways, too, can run backward: fitness markers predict individual behaviors, health status predicts fitness, and illness or injury—captured by pathway H—can abruptly reshape the whole day, as anyone with a broken leg who suddenly spends far more time sleeping and sitting can attest.

What distinguishes this framework from its predecessors is its insistence on temporal resolution. The authors divide the eight pathways into short-term dynamics operating within days, medium-term associations across weeks and months, long-term effects over years, and mixed pathways spanning days to years. Affective states fluctuate hour to hour and even minute to minute, so pathways A, B, and C demand high-granularity, real-world data. The state-of-the-art tool here is ambulatory assessment: smartphone-based electronic diaries capturing real-time self-reports, paired with wearable sensors measuring the full 24-hour behavior composition objectively and repeatedly. This approach preserves ecological validity and avoids the retrospective biases that plague questionnaire studies. By contrast, the fitness pathway E requires weeks or months of observation, ideally through randomized interventions with repeated and follow-up measurements, while the health pathways F and G unfold over years and call for long-term cohort studies—one 18-year community study found that habitual physical activity related positively to both fitness and health status decades later.

The model also arrives at a moment when the statistical machinery for 24-hour research has matured. Compositional data analysis treats sleep, sedentary time, and activity as parts of a finite whole, analyzing them relative to one another rather than as independent variables, and the compositional isotemporal substitution method estimates what happens to a health outcome when a fixed block of time is shifted from one behavior to another. The empirical payoff is already visible. In an analysis of six cross-sectional studies covering 15,253 participants, moderate-to-vigorous physical activity showed the strongest and most time-efficient protective associations with cardiometabolic outcomes, while sedentary behavior was the only behavior with clearly adverse associations regardless of duration. The Maastricht Study, with 2,388 participants, found that less sitting and more standing, physical activity, and sleep were associated with better cardiometabolic health and glycaemic control. Device-based measurement via wearables now makes it possible to capture every facet of the daily composition with time-stamped precision, and the model’s components can be refined as needed—splitting activity into standing, light, and vigorous categories, or sleep into REM and non-REM stages, or even distinguishing outdoor from indoor activity.

The long-term ambition is strikingly concrete: to identify the optimal balance of 24-hour behaviors that maximizes health benefits and promotes longevity and well-being. Tremblay and colleagues anticipate that the 24-hour approach will eventually underpin individualized, precision movement guidelines tailored to personal characteristics and circumstances—several countries and the World Health Organization already issue integrated 24-hour movement guidelines. The authors are careful to note that the relationships in their model are shaped by genetic predispositions, social and physical environments, and individual circumstances, which may moderate, mediate, or confound the pathways, so generalization demands caution. But by combining behavioral, affective, and health determinants and consequences in a single flexible framework, and by specifying exactly which timescale each hypothesis should be tested on, the 24-hour cognitive-affective physical behavior model gives researchers something previous theories did not: a shared map of the entire day, drawn to scale, with the clock built in.

Subject of Research: The 24-hour cognitive-affective physical behavior model: a theoretical framework for studying determinants and health consequences of physical activity, sedentary behavior, and sleep

Article Title: The 24-hour cognitive-affective physical behavior model: a theoretical framework for studying determinants and health consequences of physical activity, sedentary behavior, and sleep

Article References: Giurgiu, M., & Ebner-Priemer, U. W. (2025). The 24-hour cognitive-affective physical behavior model: a theoretical framework for studying determinants and health consequences of physical activity, sedentary behavior, and sleep. Journal of Activity, Sedentary and Sleep Behaviors, 4(1), Article 6. https://doi.org/10.1186/s44167-025-00077-9

Image Credits: AI Generated

DOI: 10.1186/s44167-025-00077-9

Keywords: hour, cognitive-affective, physical, behavior, model, theoretical, framework, studying, determinants, health, consequences, activity

Unlike influenza A, influenza B virus lacks a broad animal reservoir and, with it, the pandemic potential that makes influenza A a constant global security concern. But the absence of pandemic risk has never equated to clinical irrelevance. Influenza B virus drives substantial morbidity in seasonal epidemics, and its two historically circulating lineages, B/Victoria and B/Yamagata, have followed strikingly different trajectories in recent years. Surveillance has documented no confirmed naturally circulating B/Yamagata-lineage viruses since March 2020, a development widely linked to the intense non-pharmaceutical interventions of the COVID-19 pandemic era. Current influenza B activity is now overwhelmingly attributable to B/Victoria-lineage viruses, a shift with real consequences for vaccine composition and the interpretation of vaccine effectiveness studies.

The central premise of the review is that the host genome may help explain a persistent puzzle: why individuals exposed to the same virus, and receiving the same vaccine, experience markedly different outcomes. Variation in genes governing antigen presentation, innate viral sensing, interferon signaling, and host dependency or restriction factors could plausibly generate heterogeneity in susceptibility, disease severity, cross-lineage immunity, and responsiveness to immunization. This framework draws on decades of immunogenetic research in influenza A and in broader antiviral biology, but the authors stress a crucial caveat: much of what has been proposed for influenza B rests on inference rather than on direct, influenza B virus-specific human data.

At the heart of the immunogenetic hypothesis lies the human leukocyte antigen system, the protein complex responsible for presenting viral peptide fragments to T cells. Differences in HLA alleles can alter which viral epitopes are displayed, how strongly T cells respond, and consequently how efficiently an infected or vaccinated individual clears virus or mounts protective memory. For influenza A, associations between specific HLA variants and outcomes such as infection risk, severity, and antibody titers after vaccination have been reported across multiple populations. Extending these findings to influenza B is not straightforward, however, because the two virus types differ in their evolutionary dynamics, transmission patterns, and the antigenic landscape they present to the immune system. Epitope repertoires are not interchangeable, and a genetic variant that enhances clearance of one influenza type may have little or no measurable effect on the other.

Beyond antigen presentation, the review considers the innate immune machinery that first detects invading influenza viruses. Pattern recognition receptors such as the toll-like receptors and RIG-I-like receptors sense viral RNA and trigger signaling cascades that culminate in interferon production. Genetic polymorphisms in these sensors and in the downstream interferon pathway can modulate the vigor of the early antiviral response, potentially determining whether an infection is contained quickly or gains a foothold. Similarly, host dependency factors that the virus requires for entry, replication, and assembly, along with restriction factors that actively inhibit viral replication, represent additional layers where inherited variation could shape susceptibility. Each of these domains offers a plausible mechanistic route by which host genotype could influence influenza B outcomes, yet the authors find that direct evidence in the influenza B context remains sparse and fragmentary.

When it comes to vaccine response, the review is similarly measured. The best-supported determinants of influenza vaccine performance, the authors conclude, are not genetic at all. Antigenic match between vaccine strains and circulating viruses, the continuous process of antigenic drift that erodes that match over time, the age of the vaccinee, prior exposure history, and baseline immunity stand out as the factors with the strongest evidentiary grounding. These non-genetic determinants have been repeatedly validated across seasons and populations, and they explain a considerable portion of the year-to-year variability in vaccine effectiveness. Genetic predictors specific to influenza B, by contrast, remain incompletely defined, and no validated host-genetic biomarker currently exists to guide vaccination decisions for this virus.

This asymmetry between well-established extrinsic factors and poorly characterized intrinsic ones is not merely an academic gap. Predictive models of influenza B immune control and vaccine performance are limited by the absence of genotype-linked outcome data. Without large, well-phenotyped cohorts in which host genotype, immune phenotyping, and lineage-resolved virologic outcomes are collected together, the field cannot distinguish genuine genetic effects from confounding by age, prior exposure, or antigenic distance. The authors argue that such integrated studies represent the most important priority for future research, and they outline a research agenda built around linking these data streams in a single analytical framework.

The disappearance of the B/Yamagata lineage adds an unusual wrinkle to this agenda. With no naturally circulating Yamagata viruses detected for years, vaccine components targeting that lineage have become biologically obsolete, and regulatory and advisory bodies have been reconsidering the composition of seasonal vaccines, including the transition from quadrivalent to trivalent formulations. For immunogenetic studies, the loss of a circulating lineage complicates the interpretation of historical cross-lineage immunity data and underscores the need for lineage-resolved outcome measures in future cohorts. Any genetic association study conducted today will, in practice, be measuring responses against B/Victoria viruses, and generalizing those findings to influenza B as a whole carries inherent uncertainty.

Population-specific variation presents another challenge. Immunogenetic associations identified in one ancestry or geographic setting frequently fail to replicate elsewhere, reflecting both genuine differences in allele frequencies and differences in study design, exposure patterns, and co-circulating pathogens. The international composition of the review team, spanning institutions in China, Pakistan, and Uzbekistan, reflects a growing recognition that influenza B research must extend beyond the settings where it has traditionally been studied. Building the evidence base for immunogenetic determinants will require multi-center collaborations with standardized genotyping platforms, harmonized immune phenotyping protocols, and consistent definitions of susceptibility, severity, and vaccine response.

The review, which received support from the Guangdong Basic and Applied Basic Research Foundation and the Dongguan Science and Technology of Social Development Program, ultimately delivers a message of disciplined optimism. The biological logic connecting host genetic variation to influenza B outcomes is sound, and the methodological tools needed to test it, from affordable genome sequencing to sophisticated immune profiling, are now widely available. What is missing is the concerted, influenza B-specific data collection that would convert plausible mechanisms into clinically actionable knowledge. Until that work is done, antigenic match, age, and exposure history will remain the most reliable predictors of how influenza B behaves in populations, while the genome’s contribution waits to be quantified.

Subject of Research: Immunogenetic determinants of influenza B virus susceptibility and vaccine response

Article Title: Immunogenetic determinants of influenza B virus susceptibility and vaccine response: current evidence, gaps, and future directions

Article References: Din, G. U., Khan, H., Tariq, Z., Zhao, J., Khan, A., Eshboev, F., Xu, G., Hu, Y., & Huang, K. (2026). Immunogenetic determinants of influenza B virus susceptibility and vaccine response: current evidence, gaps, and future directions. Virology Journal. https://doi.org/10.1186/s12985-026-03292-1

Image Credits: AI Generated

DOI: 10.1186/s12985-026-03292-1

Keywords: influenza B virus, immunogenetics, host susceptibility, vaccine response, immune heterogeneity, antigenic drift, HLA, interferon, B/Victoria lineage, B/Yamagata lineage, Immunogenetic, determinants

Measles spreads efficiently when susceptible people are concentrated in the same population, allowing outbreaks to expand rapidly once the virus is introduced. Vaccination interrupts that chain of transmission by reducing the number of people who can become infected and pass the virus onward. The first measles-containing vaccine dose is therefore a central marker of childhood protection, although a single dose does not provide complete protection for every child. The study’s estimated coverage of 58.3%, with a 95% confidence interval of 55.5% to 61.1%, indicates that many children remained outside the reach of routine or supplementary immunization services during the period represented by the survey. The researchers describe this coverage as below the level needed for herd protection. In practical terms, low overall coverage can conceal even more serious local vulnerabilities: a national average may appear moderate while particular districts or communities have far fewer vaccinated children and therefore greater potential for sustained transmission.

The investigators analyzed records for 3,436 children aged between 12 and 59 months. The survey data were collected during fieldwork in 2018 and 2019 and formed the basis of the 2020 Somalia Demographic and Health Survey. To estimate vaccination prevalence, the researchers applied the survey’s sampling weights, including the variable V005, so that the results reflected the design of the national survey rather than simply treating every sampled record as equally representative. The outcome was coded as vaccinated or unvaccinated according to whether the child had received MCV1. Because children were sampled within households and communities, the study used multilevel logistic regression rather than a single-level model. This approach is designed to separate associations linked to individual or household characteristics from variation associated with the broader community context. It cannot prove that a particular factor causes vaccination, but it can show how strongly measured characteristics are associated with the odds of receiving a dose while accounting for clustered data.

The geographic analysis revealed a strong spatial pattern. Global Moran’s I, a statistic used to test whether similar values are arranged near one another rather than distributed randomly, was 0.653, with a probability value below 0.001. That result indicates significant positive spatial autocorrelation: areas with relatively high vaccination levels tended to be near other areas with relatively high levels, while areas with lower coverage tended to cluster with other low-coverage areas. The researchers then used Getis-Ord Gi* hotspot analysis to identify concentrations of unusually high or low values and applied SaTScan Bernoulli models to search for statistically defined geographic clusters. Together, these techniques produced a map of concentrated vaccination advantage and disadvantage. Hotspots were identified in the northern regions of Togdheer, Sool, Sanaag and Bari. Coldspots appeared in the southern regions of Gedo, Lower Juba and Bakool, where low uptake was geographically concentrated rather than isolated.

The multilevel findings add detail to that map. Children aged 48 to 59 months had substantially higher adjusted odds of having received MCV1 than the comparison age group, with an adjusted odds ratio of 16.32. An odds ratio compares the odds of an outcome between groups after accounting for other variables in the model; it is not the same as a percentage increase in vaccination coverage. The strong age association may reflect the cumulative opportunity for a child to encounter vaccination services as time passes, although the survey design cannot establish the exact reason. Maternal education was also associated with uptake. Children whose mothers had secondary education had 1.83 times the adjusted odds of vaccination compared with the reference category. The authors interpret this pattern as consistent with the potential importance of health knowledge, communication with services and the ability to navigate vaccination schedules, while recognizing that education may also be connected with other social and economic conditions.

Household economic position and mobility were likewise associated with MCV1. Children from middle-income households had 1.88 times the adjusted odds of vaccination compared with the reference wealth group. The study also found higher adjusted odds among children in nomadic residence, with an adjusted odds ratio of 1.60. That result does not mean that all nomadic families experience easier access to immunization. Instead, it shows an association within this dataset and may reflect the reach of particular outreach activities, differences in service delivery or other characteristics measured or unmeasured in the survey. Regional differences were especially large: children in Togdheer had 6.3 times higher odds of vaccination than children in the reference region. Such contrasts emphasize that national vaccination strategies cannot rely solely on uniform service delivery. The same intervention may perform differently depending on transport routes, settlement patterns, local health infrastructure and the ability of families to return for scheduled care.

The researchers quantified the contribution of community-level conditions using the intra-cluster correlation coefficient, or ICC. The ICC indicated that 13.1% of the variance in vaccination status was attributable to differences between communities. In a hierarchical dataset, this measure helps show whether children living in the same community resemble one another in their likelihood of vaccination beyond what can be explained by their individual characteristics. A community contribution of this magnitude suggests that place-based factors matter: the presence and reliability of health facilities, outreach schedules, local information networks, security conditions, transport and the organization of mobile services may all influence whether families can obtain immunization. The analysis did not measure every one of these mechanisms directly, so the ICC should not be read as identifying a single cause. Rather, it supports the conclusion that interventions focused only on household behavior would leave part of the vaccination gap untouched.

The study’s conclusions direct attention to the southern coldspots and to groups that may be missed by facility-based programs. The authors recommend prioritizing geographically identified low-coverage areas, strengthening maternal health literacy and expanding mobile vaccination outreach for underserved rural and nomadic populations. A mobile approach can bring services closer to communities whose distance, movement or limited transport options makes routine clinic attendance difficult. Geospatial results can help planners decide where such activities are most urgently needed, while multilevel findings can help tailor communication and delivery strategies to local circumstances. The analysis is based on secondary, anonymized, publicly available survey data and therefore describes associations at the population level rather than tracking children over time. Even with that limitation, its combination of weighted survey estimates, spatial statistics and hierarchical regression provides a more targeted view than a single national coverage figure. For Somalia, the message is clear: closing the measles immunity gap will require not only more doses, but also a precise understanding of where access and protection remain weakest.

The study’s design is particularly useful for distinguishing national coverage from the distribution of coverage. A weighted prevalence describes the estimated share of eligible children who had received MCV1, whereas the spatial statistics examine whether vaccination levels are arranged in recognizable geographic patterns. These answer different questions: the first indicates the overall scale of the immunity gap, while the second helps determine whether low uptake is concentrated enough to justify place-specific planning. The significant Moran’s I result and the hotspot and coldspot analyses therefore complement, rather than duplicate, the regression findings.

The multilevel framework also changes how the reported associations should be interpreted. In a conventional regression, observations from the same community may be treated as independent even though they can share health facilities, outreach teams, information sources and environmental constraints. Accounting for clustering reduces the risk that such shared circumstances will be mistaken for purely individual effects. The community-level variance identified by the investigators consequently supports a delivery-oriented interpretation: improving vaccination may require changes in how services are organized and reached, not simply stronger demand among caregivers.

Age deserves careful consideration when translating the results into program decisions. Older children have had more time to encounter routine services or catch-up activities, so the age association may signal delayed rather than absent access. It also suggests that programs should actively identify children who have passed the usual vaccination contact point without receiving MCV1. This is different from assuming that younger children are inherently less likely to benefit; they may simply have had fewer opportunities by the time of observation. The cross-sectional survey cannot establish the sequence of events behind the association.

Similarly, the findings on education, wealth and residence should guide investigation rather than serve as fixed classifications of families. These characteristics can be connected to several pathways at once, including awareness, affordability of travel, trust, service availability and the timing of outreach. The study’s adjusted odds ratios show relationships after inclusion of measured variables, but they do not identify which pathway is responsible or demonstrate that changing one characteristic would produce a specific increase in uptake. Local follow-up could therefore help determine whether the most effective response is communication, transport support, improved session reliability, or a combination of approaches.

Because the analysis used existing anonymized survey data, it provides a broad population-level assessment without exposing participants to additional data collection. Its findings can establish priorities for vaccination planning, but implementation should still be accompanied by monitoring. Repeated coverage assessments and evaluation of outreach in the designated low-coverage areas would help determine whether geographic inequalities narrow over time and whether improvements reach children who remain unvaccinated, rather than merely shifting the national average.

Subject of Research: Geographic and social determinants of first-dose measles vaccination among children in Somalia

Article Title: Spatial patterns and determinants of childhood measles vaccination in Somalia a multilevel and geospatial analysis of the 2020 demographic and health survey

Article References: Abdillahi, A. M., Ali, I. H., Omer, H. A., Said, N. I., Bile, H. S., & Muse, A. H. (2026). Spatial patterns and determinants of childhood measles vaccination in Somalia a multilevel and geospatial analysis of the 2020 demographic and health survey. Discover Social Science and Health. https://doi.org/10.1007/s44155-026-00416-4

Image Credits: AI Generated

DOI: 10.1007/s44155-026-00416-4

Keywords: Measles vaccination, Somalia, MCV1, Child health, Geospatial analysis, Multilevel modeling, Vaccine coverage, Health inequities, Spatial, patterns, determinants, childhood