Correction Updates Flexibility Estimate in Ibero-American 24-Hour Movement Study — What the Change Means for Interpreting Youth Fitness During COVID-19

Table of Contents

1. Key Highlights:
2. Introduction
3. What changed and why it matters
4. How to read the revised table: odds ratios, MOR and ICC explained
5. What the corrected figures imply about 24-hour movement behaviors and youth fitness during COVID-19
6. Country-level differences: where heterogeneity matters and why
7. Measurement matters: self-reported fitness and 24-hour movement behavior in youth
8. Why a single numeric correction matters for science and policy
9. Practical takeaways for researchers, funders and policymakers
10. Broader lessons from pandemic-era research on child movement and fitness
11. Recommendations moving forward
12. FAQ

Key Highlights:

• A published correction updates the median odds ratio (MOR) for flexibility from 0.6 to 1.14 in Table 5 of the study examining 24-hour movement behaviors and self-reported fitness among Ibero-American children and adolescents during the COVID-19 pandemic; the correction also fixes wording in the table footnote about model adjustment.
• The corrected figures confirm low between-country variability for flexibility but highlight larger country-level differences for muscular fitness; most adjusted odds ratios in the table remain imprecise and cross unity, underscoring cautious interpretation of associations between combined 24-hour movement behaviors and self-reported fitness outcomes.

Introduction

A formal correction to a Frontiers in Public Health article altered a single numeric entry in a key results table. On its face, the change looks small: the median odds ratio for country-level variation in flexibility rose from 0.6 to 1.14. Yet that single entry speaks to how statistical summaries shape interpretation of population differences and how researchers, policymakers and practitioners rely on precise reporting to make decisions.

The corrected table comes from an analysis of the relationship between combined 24-hour movement behaviors — sleep, sedentary time and physical activity — and self-reported physical fitness among preschoolers, children and adolescents across Ibero-American countries during the COVID-19 pandemic. The original study sought to examine whether meeting recommended patterns across all three movement components related to several fitness domains, using multilevel models that accounted for clustering by country. The correction clarifies the degree to which country-level factors contributed to variance in flexibility and refines the footnote wording about model covariates.

This article unpacks the correction, explains the statistical concepts affected, places the results in the context of pandemic-era research on child movement and fitness, and outlines practical implications for researchers and policymakers working on child health surveillance and interventions in multinational settings.

What changed and why it matters

The correction revises two elements in Table 5 of the published article. First, the median odds ratio (MOR) reported for flexibility was updated from 0.6 to 1.14. Second, the table footnote was reworded to use “Adjusted for” rather than “Adjusted by” when describing covariate control in the multilevel models.

The MOR is a statistic derived from the random-effects variance component in multilevel models; it quantifies the median increase in odds of an outcome when comparing two randomly chosen clusters — in this case, countries — with different risk profiles. The previously published MOR of 0.6 for flexibility suggested a value below 1, which is atypical because MORs are defined as equal to or greater than 1. The corrected MOR of 1.14 restores the expected property and indicates a modest degree of between-country heterogeneity for flexibility (corresponding with an intraclass correlation coefficient, ICC, of 0.6%). The footnote wording change clarifies the phraseology and aligns with conventional reporting.

Why does this matter? Scientific evidence builds on precise reporting. A misreported MOR can create confusion about the presence and magnitude of cluster-level effects. In a study that intentionally models country as a random effect, accurate reporting of variance, MOR and ICC is essential for understanding how much outcomes vary across countries and whether interventions should be targeted at national or broader levels.

The correction does not alter the point estimates for the odds ratios comparing combinations of 24-hour movement behaviors to fitness outcomes. Those values — many of them with wide confidence intervals crossing 1 — remain unchanged. The key shift is interpretive: flexibility shows minimal country-level heterogeneity but does register a MOR above 1 after correction; muscular fitness, by contrast, retains a much larger country-level variance and MOR.

How to read the revised table: odds ratios, MOR and ICC explained

The corrected table presents adjusted odds ratios (ORs) for the association between combinations of 24-hour movement behaviors (meeting all three components versus meeting two, one, or none) and five self-reported fitness outcomes: general fitness, muscular fitness, cardiorespiratory fitness, speed/agility and flexibility. Two models appear: Model 1 adjusts for sex, age and breadwinner’s educational level; Model 2 adds country as a covariate (or models country-level effects explicitly). The table also reports country-level random-effects variance, MOR and ICC for each outcome.

Odds ratios and confidence intervals

• An OR greater than 1 indicates higher odds of the self-reported fitness outcome in the exposure group relative to the reference; an OR less than 1 indicates lower odds. Confidence intervals quantify sampling uncertainty. When a 95% confidence interval includes 1, the association is not statistically significant at the conventional 0.05 level.
• The table shows many ORs with wide confidence intervals that span 1, reflecting imprecision. Wide intervals result from limited sample sizes, substantial heterogeneity in self-report measures, or both.

Random-effects variance, MOR and ICC

• Random-effects variance captures the between-country variability in the log-odds of the outcome that is not explained by the fixed effects in the model.
• The MOR translates that variance into an interpretable metric on the odds scale. An MOR of 1 indicates no between-country variation. Values above 1 indicate the median multiplicative difference in odds when moving between a higher- and lower-risk country.
• The ICC expresses the proportion of total variance attributable to clustering within countries.

Interpretation for the five outcomes

• Muscular fitness shows the largest country-level variance (0.71), an MOR of 2.23 and an ICC of 17.8%. Those numbers imply meaningful heterogeneity across countries: a child’s odds of reporting higher muscular fitness can vary substantially depending on country context.
• Cardiorespiratory fitness and flexibility show minimal country-level variance (0.03 and 0.02, respectively), with corresponding low ICCs (0.9% and 0.6%). The corrected MOR for flexibility is 1.14, indicating a small but non-negligible country effect.
• General fitness and speed/agility fall between these extremes: general fitness has variance 0.19 and MOR 1.52 (ICC 5.5%), while speed/agility has variance 0.08 and MOR 1.31 (ICC 2.4%).

The contrast between muscular fitness and flexibility underscores how different fitness domains carry different susceptibilities to country-level determinants — from policy and infrastructure to cultural norms and pandemic response measures.

What the corrected figures imply about 24-hour movement behaviors and youth fitness during COVID-19

The study compares children and adolescents who met the combination of all three recommended 24-hour movement behaviors against those who met only two, only one, or none. The ORs in the table represent relative odds of higher self-reported fitness across those groupings.

Across outcomes, point estimates vary. For example, children meeting only one or none of the recommended components sometimes show higher point estimates for certain fitness outcomes relative to those meeting all three. Those patterns may appear counterintuitive but must be interpreted cautiously because most estimates are imprecise.

Key interpretive points:

• Lack of consistent, precise associations: Many confidence intervals are wide and include 1. The absence of precise, statistically significant associations suggests that the cross-sectional, self-reported relationships between combined 24-hour behaviors and perceived fitness in this sample were not strong or clear-cut during the pandemic period.
• Domain-specific variation: The pattern of point estimates differs by fitness domain. Flexibility shows elevated point estimates for one-component and none groups (Model 2 OR 2.55 and 2.58 respectively), but large confidence intervals indicate uncertainty. Muscular fitness shows point estimates slightly above 1 for one and none groups but again no precision. Cardiorespiratory fitness shows ORs below 1 in many groupings, consistent with the expectation that lower adherence might associate with lower cardiorespiratory fitness, but intervals are wide.
• Potential measurement and contextual explanations: The pandemic disrupted typical routines for children and adolescents. Home confinement, loss of structured physical education, unequal access to safe outdoor space, changes in sleep patterns and increased screen time all interacted to shape movement behaviors. Self-perceived fitness measures may reflect subjective judgments that are not tightly coupled to objective fitness performance, especially in younger children.

Those observations underline that evidence linking 24-hour movement profiles to self-reported fitness during an unprecedented social disruption is inherently complex. The corrected table does not overturn the study’s broader investigative framing; rather it clarifies the magnitude of between-country differences for flexibility and reinforces caution around overinterpreting unstable point estimates.

Country-level differences: where heterogeneity matters and why

A central methodological choice in the original analysis was to include country as a random effect. That choice recognizes that children are nested within national contexts that shape behavior and health outcomes through policy, culture, built environment and socioeconomic conditions.

Muscular fitness: a notable country effect

• The muscular fitness outcome displays the most pronounced between-country variance. An MOR of 2.23 means the median difference in odds of reporting higher muscular fitness between two randomly selected countries is more than double.
• An ICC of nearly 18% indicates that nearly one in five units of variance in muscular fitness arises from between-country differences rather than individual-level variation or other modeled covariates.
• This pattern suggests muscular fitness is particularly sensitive to factors that vary at national scales: national sports curricula, availability of after-school programs, prevalence of organized youth sports, and infrastructure for physical activity.

Flexibility and cardiorespiratory fitness: limited country variance

• Flexibility and cardiorespiratory fitness show small random-effects variance and low ICC values. These domains may be less dependent on national policy differences and more influenced by home environments, short-term activity patterns, genetics, or measurement error in self-report.
• The corrected MOR of 1.14 for flexibility indicates some between-country variation, albeit modest.

Implications for policy and program design

• When country-level variation is large, interventions that focus solely at the individual or community level may fail to address systemic drivers. A higher MOR for muscular fitness argues for national strategies — curriculum reform, investment in school-based strength and motor skill programs, and national campaigns promoting muscular development in youth.
• Where country-level variance is low, regional or local initiatives tailored to community needs could be more efficient.

Different pandemic responses, different child experiences

• During the COVID-19 pandemic, countries diverged in the timing and strictness of lockdowns, school closures, allowances for outdoor exercise, and support for families. Those policy choices likely contributed to heterogeneous effects on children’s opportunities for physical activity and structured fitness development.
• Even absent precise data tying individual policy decisions to the variance components, recognizing that muscular fitness strongly tracks national context helps target surveillance and resource allocation in multinational studies.

Measurement matters: self-reported fitness and 24-hour movement behavior in youth

The study’s outcomes rely on self-reported measures of physical fitness. Self-report, especially in children and adolescents, carries specific limitations that affect interpretation.

Challenges with self-report in youth

• Comprehension and recall: Young children may struggle to understand survey items concerning fitness domains or to recall habitual behaviors accurately. Parental proxy reports help for preschoolers but introduce their own biases.
• Social desirability and subjective calibration: Respondents may overestimate or underestimate their fitness based on perceived norms, body image, or current mood.
• Domain specificity: Perceptions of muscular fitness, cardiorespiratory capability, speed/agility and flexibility may not align with objective measures such as grip strength, shuttle run results or sit-and-reach scores.
• Differential validity across ages: Adolescents may report with greater accuracy than preschoolers; combining these age groups increases variability.

Measurement of 24-hour movement behavior

• The study assessed whether children met recommended thresholds across sleep, sedentary behavior (often screen time), and physical activity. How these behaviors were measured influences the robustness of associations.
• Objective measurement using accelerometers or wearable devices yields richer, more reliable profiles of activity intensity and sleep, but these devices were often impractical during pandemic fieldwork, particularly in large multinational samples.
• Self-report or parent-report of time spent in activities and sleep introduces bias and measurement error, which tends to attenuate observed associations.

Consequences for interpretation

• Measurement error in exposures and outcomes leads to misclassification, often biasing estimates toward the null. The wide confidence intervals observed in the table may reflect such error.
• When evaluating policy or program impacts, prioritize studies that include objective fitness and movement measures or validate self-report instruments against objective benchmarks in similar populations.

Why a single numeric correction matters for science and policy

Corrections to published research should be considered part of healthy scientific practice. Numerical accuracy underpins meta-analyses, systematic reviews, evidence syntheses and policy recommendations.

Statistical integrity

• The MOR is mathematically constrained to values of one or higher. A reported MOR less than one denotes a reporting or calculation error. Correcting that restores internal consistency.
• The presence of one misreported statistic raises questions about proofreading, data handling and editorial oversight. A transparent correction process preserves trust.

Impact on interpretation and decisions

• For most readers the correction does not alter substantive conclusions about the relationship between combined movement behaviors and fitness outcomes in this sample. The main patterns — imprecise ORs and domain-specific country variance — remain.
• For analysts conducting pooled analyses or policy briefs that rely on accurate variance components, precise values matter. Variance estimates inform sample size calculations, anticipated heterogeneity, and the design of future multinational studies.

Communicating uncertainty

• Corrections are not admissions of failure but reflections of iterative improvement. Authors and journals that correct the record help consumers of research make better-informed decisions.
• When nations, ministries, or international agencies examine comparative data, small numerical differences can shift priority setting. Accurate reporting of heterogeneity metrics supports appropriate targeting.

Practical takeaways for researchers, funders and policymakers

The corrected table and its interpretation yield actionable insights for those engaged in child health surveillance, program design and policy.

Designing future studies

• Incorporate objective measures where feasible. Accelerometers, standardized field-based fitness tests and validated questionnaires reduce measurement error and improve precision.
• Plan for clustering at country or regional levels. High ICCs in outcomes such as muscular fitness mean study designs must account for cluster effects in power calculations and analytic strategies.
• Stratify analyses by age group to account for developmental differences in self-report validity and fitness determinants.

Policy and program responses

• When country-level variance is substantial, develop national strategies that address systemic determinants: curriculum standards for physical education, investment in safe play spaces, and national campaigns to promote strength-building activities in schools.
• At the community level, prioritize programs that can be rapidly deployed during disruptive events. For example, remote or home-based strength and motor skill curricula can mitigate losses when in-person programming is interrupted.

Monitoring and evaluation

• Establish routine, standardized surveillance of youth movement and fitness that enables cross-country comparisons without methodological artifacts. Shared protocols and harmonized instruments reduce heterogeneity driven by measurement rather than true differences.
• Monitor domains separately. The evidence indicates domain-specific responses to context; cardiorespiratory fitness, muscular fitness and flexibility do not respond identically to policy and environmental conditions.

Equity considerations

• Pandemic impacts on movement behaviors were unevenly distributed across socioeconomic strata. Programs should target underserved communities with interventions that reduce barriers to safe physical activity and structured fitness development.

Communication and public trust

• Transparently report corrections and methodological details. Clear communication about what changed and why supports public trust in science and the evidence base guiding child health policy.

Broader lessons from pandemic-era research on child movement and fitness

The COVID-19 pandemic presented a natural experiment in which children’s movement patterns, structured activities and social environments shifted dramatically. Studies conducted during this period reveal both vulnerabilities and opportunities.

Resilience and adaptability

• Some children maintained or adapted activity behaviors through home-based play, family walks, or online physical activity programs. Those adaptive strategies provide models for maintaining activity during future disruptions.
• Schools and community organizations that rapidly deployed remote physical education or packaged activity kits demonstrated feasible approaches to sustain movement when access to typical settings was limited.

Data gaps and methodological innovation

• The pandemic highlighted the fragility of surveillance systems. Many countries lacked timely, standardized data on child movement behaviors and fitness.
• Research teams innovated with remote data collection, parent-reported surveys, and device-based measures when possible. Those methodological advances can inform long-term surveillance systems.

Policy preparedness

• Preparedness plans for future public health emergencies should explicitly include provisions for maintaining child physical activity opportunities, including allowances for outdoor exercise, guidance for safe play, and investment in virtual or distributed programming.

Global collaboration

• Multinational studies provide critical insights but require harmonized protocols and attention to cross-country variance. The corrected table underscores the importance of modeling country-level effects and interpreting them cautiously.

Recommendations moving forward

From the perspective of a researcher or policymaker tasked with improving child fitness and movement behaviors, the corrected findings suggest several practical steps.

For researchers

• Validate self-report instruments against objective tests in representative sub-samples.
• Report variance components, MOR and ICC alongside fixed effects routinely in multinational studies.
• Pre-register analytic plans and provide reproducible code to facilitate verification.

For funders

• Support infrastructure for standardized, longitudinal surveillance of child movement and fitness across countries.
• Fund capacity-building for low-resource settings to implement device-based measurement and rigorous monitoring.

For policymakers and practitioners

• Prioritize muscular fitness interventions in national strategies where country-level variance is high. Support teacher training and curriculum materials that incorporate strength and motor skill development.
• Maintain access to safe outdoor spaces and consider equity-focused subsidies to reduce barriers for disadvantaged families.
• Develop contingency plans to sustain physical activity supports during school closures or public health emergencies.

FAQ

Q: Does the correction change the study’s primary conclusions? A: The correction adjusts a country-level heterogeneity measure for flexibility and clarifies table wording. It does not change the reported associations between combined 24-hour movement behaviors and self-reported fitness outcomes. Interpretation remains cautious because many odds ratios are imprecise and confidence intervals span unity.

Q: What is the median odds ratio (MOR) and why is it useful? A: The MOR translates between-cluster variance from multilevel models into an odds ratio metric. It represents the median increase in odds of the outcome when comparing two randomly selected clusters (countries) with differing risk. An MOR greater than 1 indicates meaningful cluster-level heterogeneity. It complements the ICC by providing a multiplicative measure on the odds scale.

Q: What does an intraclass correlation coefficient (ICC) of 17.8% for muscular fitness mean? A: An ICC of 17.8% indicates that about 17.8% of the total variance in muscular fitness in the sample is attributable to differences between countries rather than individual-level variation. That magnitude signals substantial between-country heterogeneity requiring attention in analysis and policy design.

Q: Many odds ratios have wide confidence intervals and include 1. How should those be interpreted? A: Wide intervals indicate imprecision and uncertainty in the estimates. When intervals include 1, no statistically significant association can be claimed at conventional thresholds. Analyses with larger, better-measured samples or objective fitness outcomes may yield clearer results.

Q: Why might muscular fitness show greater country-level variance than flexibility or cardiorespiratory fitness? A: Muscular fitness development often depends on structured opportunities, cultural emphasis on strength activities, national sports and physical education curricula, and access to facilities or trained instructors. These features vary substantially at the national level and can produce larger between-country variance. Flexibility and cardiorespiratory fitness may be less sensitive to national-level policies or more influenced by immediate home and play environments.

Q: How reliable are self-reported fitness measures in children? A: Reliability varies by age and domain. Adolescents can often self-report with reasonable accuracy; younger children require parental proxy reporting, which introduces different biases. Self-reports capture perceived fitness, which may diverge from objective performance tests. For robust surveillance and intervention evaluation, complement self-report with objective measures when feasible.

Q: Does the corrected MOR of 1.14 for flexibility indicate a meaningful national effect? A: An MOR of 1.14 indicates a small between-country effect for flexibility; on average, country-level differences modestly alter the odds of reporting higher flexibility. The associated ICC of 0.6% confirms that only a small fraction of variance is at the country level for this domain.

Q: What are practical next steps for public health agencies concerned about child fitness post-pandemic? A: Implement standardized monitoring of movement and fitness that includes objective measures where possible; invest in school-based muscular fitness programs and teacher training; address inequities in access to safe play spaces; prepare contingency plans for sustaining physical activity during future disruptions.

Q: Where can I find the corrected article and table? A: The correction and the updated article appear in Frontiers in Public Health (Volume 14, 2026). The corrected table is included in the published correction notice and the original article has been updated online.

Q: How common are corrections like this in peer-reviewed journals? A: Corrections are a routine part of scientific publishing and reflect attention to accuracy. They range from typographical fixes to substantive updates. Transparent corrections preserve the integrity of the record and support ongoing scientific discourse.

Precise reporting of statistical details matters as much as the substantive findings they support. The corrected MOR for flexibility restores internal consistency and clarifies country-level heterogeneity in a multinational study of child movement behaviors and self-reported fitness during an extraordinary global event. The overall pattern of imprecise associations and domain-specific variability points to measurement and contextual complexities that researchers must address in designing future studies and that policymakers must consider when targeting interventions.