Physical activity, sleep and screen time during COVID-19: how 24‑hour movement behaviors shaped self‑reported fitness in Ibero‑American youth

Table of Contents

1. Key Highlights
2. Introduction
3. How the pandemic reshaped children’s daily movement: context from three countries
4. How 24‑hour movement behaviors were measured and analyzed
5. Physical activity: the strongest and most consistent link to self‑reported fitness
6. Screen time and sleep: inconsistent associations that require nuanced interpretation
7. Meeting multiple recommendations: combined adherence matters most for older children and adolescents
8. Country context and socioeconomic factors: where national policy and household realities intersect
9. Strengths and limitations of the study
10. What this means for recovery policies and practice
11. Research priorities going forward
12. Practical steps for parents, educators and health professionals
13. Final takeaways
14. FAQ

Key Highlights

• Across 1,077 preschoolers, children and adolescents from Spain, Brazil and Uruguay, meeting the daily physical‑activity recommendation was the behavior most consistently linked to higher self‑reported physical fitness; associations strengthened with age.
• Screen time and sleep showed weaker, less consistent relationships with fitness; failing to meet multiple 24‑hour movement recommendations was associated with poorer fitness mainly in children and adolescents.
• Country context influenced some outcomes (notably muscular fitness in children), but most variance in fitness was explained at the individual level, underscoring the need for locally tailored, activity‑focused recovery strategies.

Introduction

Schools, sports clubs and playgrounds closed or operated at reduced capacity across much of 2020–2021. Those disruptions cut into organized physical activity and reordered children’s days — more time on screens, more variable sleep, less structured movement. Researchers from Spain, Brazil and Uruguay used that natural experiment to examine how three core behaviors — daily physical activity (PA), recreational screen time (ST) and sleep duration — related to self‑reported physical fitness among preschoolers, children and adolescents during the first months of the COVID‑19 pandemic.

Their cross‑sectional analysis of 1,077 youth drew on the Canadian 24‑hour movement guidelines and WHO recommendations to classify behavior adherence, and used the International Fitness Scale (IFIS) to capture perceived fitness across five domains: general fitness, cardiorespiratory fitness, muscular fitness, speed/agility and flexibility. The pattern was clear: physical activity dominated the relationship with perceived fitness. Screen time and sleep mattered less frequently in adjusted models, and combined non‑adherence carried the heaviest penalties for older age groups.

The results matter because physical fitness in childhood predicts future cardiometabolic health and sustained active behaviors into adulthood. The study isolates which daily behaviors to prioritize when restoring healthy routines after disruption and offers evidence for parents, educators and policymakers designing recovery policies that protect children’s long‑term health.

How the pandemic reshaped children’s daily movement: context from three countries

Pandemic mitigation policies varied across Ibero‑America, and those differences shaped opportunities for movement. Remote learning removed daily structure that schools provide: active commutes, physical education, recess and after‑school sports. The Pan American Health Organization described the period as an educational crisis in the region, and UNICEF reported rising stress among adolescents. Those societal changes coincided with a widespread decline in PA, more screen exposure and altered sleep schedules among children and adolescents globally.

The study’s sample reflects those shifts.

• Uruguay reported the highest proportion of participants meeting PA recommendations (65.8%), while Brazil had the lowest (22.5%).
• Screen time recommendations were unmet by the overwhelming majority (94.6% overall), with Uruguay showing 100% non‑adherence in the sample.
• Sleep adherence varied: Brazil had the highest compliance (76.4%), Uruguay the lowest (50.7%).

These country differences hint at how local policy, household routines and access to outdoor space can offset or exacerbate the impact of restrictions on movement behaviors. The analysis adjusted for country effects to separate shared regional patterns from national context.

How 24‑hour movement behaviors were measured and analyzed

The research used a parent‑reported online survey distributed via social media and snowball sampling during 2020 (March in Spain, April in Brazil, November in Uruguay). Inclusion criteria targeted children aged 3–17; the final analytic sample comprised 1,077 participants (148 preschoolers, 509 children, 420 adolescents).

Behavior classifications followed established guidelines:

• Physical activity: parents reported the number of days their child accumulated at least 60 minutes of activity. Meeting the recommendation required 60 minutes on all seven days.
• Screen time: three items captured recreational TV/video, gaming and other electronic device use; responses were weighted by weekday/weekend. For preschoolers the WHO ≤1 hour/day threshold applied; for children and adolescents the Canadian ≤2 hours/day guideline was used.
• Sleep: parents reported usual bed and wake times for weekdays and weekends; average nightly sleep was compared against age‑specific ranges (e.g., preschoolers 10–13 h, children 9–11 h, adolescents 8–10 h).

Self‑reported fitness came from the International Fitness Scale (IFIS) — a brief, validated five‑item Likert instrument — recoded into high (good/very good) versus not high (very poor/poor/medium) for each domain.

Analytical approach:

• Generalized linear mixed models estimated odds ratios (OR) for low self‑reported fitness associated with not meeting each guideline, adjusted for sex, age and the breadwinner’s education level.
• Country was introduced as a random effect to account for clustering and unobserved national influences.
• Age‑stratified models examined differences across preschoolers, children and adolescents.
• The intraclass correlation coefficient (ICC) and median odds ratio (MOR) quantified between‑country variability.

This combination permits both pooled inferences across the Ibero‑American sample and assessment of how relationships change with age and national context.

Physical activity: the strongest and most consistent link to self‑reported fitness

Physical activity emerged as the dominant driver of perceived fitness across age groups. Associations were clearest and grew in magnitude with age.

Preschoolers (3–4 years)

• Not meeting the PA recommendation (not achieving 60 minutes every day) was associated with markedly higher odds of low general physical fitness after covariate adjustment and accounting for country (OR = 3.44; 95% CI: 1.20–9.88).
• Other fitness domains did not show significant relationships with PA for preschoolers, perhaps because early childhood fitness assessments and parental perceptions are less sensitive to specific domains or because activity patterns are more variable at this age.

Children (5–13 years)

• Children who did not meet PA recommendations had greater odds of low general fitness (Model 2 OR = 2.13; 95% CI: 1.29–3.53).
• Muscular fitness (OR = 1.87; 95% CI: 1.12–3.12) and speed/agility (OR = 2.09; 95% CI: 1.25–3.51) were also associated with insufficient activity after adjusting for country.
• These patterns point to activity’s role in shaping both strength and motor skill–related components of fitness in primary school years.

Adolescents (14–17 years)

• The associations were strongest in adolescents. Not meeting PA guidelines linked to higher odds of low general fitness (OR = 3.53; 95% CI: 1.69–7.41), cardiorespiratory fitness (OR = 3.52; 95% CI: 1.41–7.26), speed/agility (OR = 3.36; 95% CI: 1.54–7.32) and flexibility (OR = 2.94; 95% CI: 1.53–5.64).
• Cardiorespiratory and agility domains showed odds ratios above 3, indicating a pronounced perceived deficit among adolescents not meeting daily activity targets.

Why does PA show the largest effect?

• Physiological responsiveness: moderate‑to‑vigorous activity stimulates cardio‑respiratory adaptations and neuromuscular development, the core determinants of many fitness components.
• Cumulative exposure: older children and adolescents have had more years to accumulate fitness gains or losses related to habitual activity.
• Behavioral patterning: school sports, extracurricular teams and independent exercise become more influential in adolescence; disruption to these outlets during lockdowns may therefore produce larger perceived declines.

Real‑world example: a 15‑year‑old who loses access to football practice and active school commuting will quickly notice declines in endurance and speed, reflected in survey responses that compare performance to peers.

Policy implication: prioritize restoring opportunities for moderate‑to‑vigorous PA for school‑aged children and adolescents. Reinstating PE, safe outdoor time, active transport and after‑school programs will have a disproportionate impact on fitness recovery.

Screen time and sleep: inconsistent associations that require nuanced interpretation

Screen time and sleep produced fewer and less consistent associations with self‑reported fitness across age groups.

Screen time

• Overall prevalence of excessive recreational screen time was extremely high: 94.6% did not meet ST guidelines, with Uruguay in the sample showing 100% non‑adherence.
• For adolescents, not meeting ST recommendations was associated with lower odds of high cardiorespiratory fitness in fully adjusted models (OR = 0.35; 95% CI: 0.12–0.96) — an unexpected direction indicating that adolescents with low screen time had higher odds of high cardiorespiratory fitness, or vice versa depending on coding and sample distributions.
• Across preschoolers and children, ST showed no consistent association with fitness domains in adjusted models.

Why the inconsistent signal?

• Low variability and ceiling effects: with nearly all participants exceeding ST guidelines, comparisons become difficult. Limited numbers who meet the guideline reduce statistical power and can produce unstable estimates.
• Heterogeneity of screen use: not all screen time is equal. Active video games or movement‑based digital play differ from passive streaming. The survey aggregated recreational screen time without distinguishing intensity or posture.
• Mediating behaviors: excessive screen time is often associated with reduced PA and poorer sleep, making it difficult to isolate an independent effect on fitness.

Sleep duration

• Sleep adherence varied by country; Brazil had the highest proportion meeting guidelines, Uruguay the lowest.
• Among children, missing sleep recommendations was associated with low muscular fitness in partially adjusted models (Model 1 OR = 1.91; 95% CI: 1.11–3.18) but lost significance after accounting for country (Model 2).
• Across adolescents, sleep showed no significant associations with fitness domains.

Interpretation

• Sleep quality and timing may matter more than duration alone. The study measured nightly duration but not continuity, variability, or daytime sleepiness — all influential for recovery, performance and behavior.
• Pandemic schedules created mixed effects: some children slept longer with later bedtimes, while others experienced dysregulated sleep. Combining these divergent changes washes out simple associations with fitness.

Real‑world example: a teenager who trades early morning soccer practice for late‑night gaming may maintain duration yet lose sleep regularity and miss high‑intensity training; duration alone will not capture that nuance.

Research implication: future studies should measure sleep quality, circadian timing and variability, and disaggregate screen behaviors — passive vs interactive — to clarify their independent and mediating roles.

Meeting multiple recommendations: combined adherence matters most for older children and adolescents

The 24‑hour movement framework emphasizes combined adherence to PA, limited ST and adequate sleep. The study assessed whether meeting more components produced better self‑reported fitness.

Pooled analyses across all ages:

• When the entire sample was examined using meeting all three recommendations as the reference, meeting fewer components did not show statistically significant associations with low fitness in fully adjusted models. Low prevalence of full adherence (only 1.4% met all three) hampered statistical comparisons.

Age‑stratified findings:

• Preschoolers: no significant associations between the number of recommendations met and fitness domains.
• Children: compared with meeting two guidelines (reference), meeting only one raised odds for low general fitness (OR = 1.74; 95% CI: 1.06–2.85), cardiorespiratory fitness (OR = 1.88; 95% CI: 1.11–3.17) and speed/agility (OR = 1.73; 95% CI: 1.03–2.90). Not meeting any recommendation increased odds of low muscular fitness (OR = 2.36; 95% CI: 1.04–5.37).
• Adolescents: effects were stronger and broader. Adolescents meeting only one recommendation had elevated odds of low general, muscular and cardiorespiratory fitness. Adolescents meeting none faced the highest likelihoods of low fitness across multiple domains (general, muscular, cardiorespiratory, speed/agility, flexibility), with ORs clustering between about 2.3 and 2.8.

Interpretation

• The combined deficit of behaviors compounds fitness risk in older youth. Adolescents appear particularly vulnerable: failing to conform to multiple movement behaviors correlates with wide‑ranging perceived declines in fitness.
• For younger children, single behaviors (notably PA) may drive outcomes more strongly than combined adherence; developmental differences in how behaviors influence fitness likely explain age patterns.

Practical angle: intervention packages that address multiple behaviors (increase PA, reduce recreational screens, stabilize sleep timing) should target adolescents and school‑aged children, while early childhood programs can emphasize daily active play.

Country context and socioeconomic factors: where national policy and household realities intersect

The models included country as a random effect and quantified between‑country variability.

Key observations:

• ICCs were generally low, meaning most variability in perceived fitness arose at the individual level rather than being explained by national differences.
• Exceptions emerged: muscular fitness among children showed substantial between‑country variability (ICC ≈ 24.5%), suggesting national contexts influenced this domain more heavily.
• Distributional differences: Uruguay participants had higher PA adherence yet universal ST non‑adherence in the sample; Brazil showed higher sleep compliance but lower PA compliance. Spain’s participants were more likely to be adolescents and had higher proportions of low breadwinner educational attainment in this dataset.

How context matters

• Policy choices (stringency and timing of lockdowns), access to private outdoor space, cultural norms around play and sport, and economic resources all determine routine and opportunity. For example, a child living in a household with a yard may sustain more daily activity during lockdown than one in a high‑density apartment.
• Socioeconomic status (proxied by breadwinner education) correlated with behaviors and fitness in other studies; here it was adjusted for, but it remains a key target for equitable recovery policies.

Policy takeaway: while individual behavior change remains central, national and local policies that preserve safe outlets for activity during crises (e.g., safe, distanced outdoor time; staggered PE; community programming) matter for muscular fitness outcomes and equity.

Strengths and limitations of the study

Strengths

• Cross‑national sample spanning preschoolers to adolescents in three Ibero‑American countries provides breadth across developmental stages and cultural contexts.
• Integration of three movement behaviors within a 24‑hour framework aligns with contemporary guidelines and reflects the interdependence of activity, sedentary time and sleep.
• Use of validated instruments (IFIS) and multilevel statistical models strengthened internal validity, and the analysis reported both pooled and age‑stratified effects.

Limitations

• Cross‑sectional design prevents causal inference. The associations describe concurrent relationships during early pandemic restrictions rather than the trajectory of change.
• Self‑reported parent proxy measures introduce recall and social desirability bias. Objective measures (accelerometry, actigraphy, direct fitness testing) would provide stronger validity but were infeasible during early lockdowns.
• Low prevalence of meeting all three recommendations limited statistical power for combined adherence comparisons.
• The screen time measure aggregated distinct activities. Interactive movement games, passive viewing and classroom device use have different implications for energy expenditure and sleep.
• Sample recruitment via social media and snowballing may bias toward more connected households and underrepresent marginalized populations without internet access.

The authors candidly acknowledge these weaknesses while emphasizing the study’s contribution to a sparse evidence base on 24‑hour movement behaviors and fitness during the pandemic in Ibero‑America.

What this means for recovery policies and practice

The findings provide actionable guidance for stakeholders working to restore healthy routines after periods of disruption.

Prioritize daily moderate‑to‑vigorous physical activity

• PA showed the strongest and most consistent link to perceived fitness, particularly for adolescents. Recovery plans should reinstate or expand PE, active transport (walking/cycling), safe youth sports and unstructured outdoor play.
• Schools can adopt targeted catch‑up programs: brief daily activity breaks, focused cardiorespiratory training, and motor skill sessions that rebuild muscular strength and speed/agility.

Address screen time with nuance

• Blanket screen‑reduction messages are insufficient. Policies should distinguish recreational, educational and interactive movement screen use.
• Practical household strategies: set screen‑free bedtime routines, move gaming consoles away from bedrooms, and schedule screen breaks with active alternatives.

Stabilize sleep timing and quality

• Reinstate consistent wake and sleep schedules aligned with school start times. Sleep interventions that target timing, light exposure and pre‑bed routines will support recovery of physical and cognitive performance.
• For adolescents, prioritize delayed school start times where feasible; evidence links later starts to improved sleep and daytime functioning, including readiness for physical activity.

Design combined behavior interventions for adolescents

• Adolescents who failed multiple recommendations showed the broadest deficits. Integrated programs that bundle activity, sleep hygiene and screen management will deliver multiplicative benefits.
• Community examples: after‑school programs combining sport with sleep education and digital‑wellness sessions; youth mentoring that links daily activity goals to social supports.

Tailor interventions to local context and equity concerns

• Country‑level differences in muscular fitness among children suggest national policy levers influence outcomes. Local authorities must adapt evidence‑based strategies to community resources, urban form and cultural norms.
• Ensure access for disadvantaged families: free or low‑cost programming, safe public spaces, and school‑centered initiatives that reduce financial and logistic barriers.

Research priorities going forward

The pandemic highlighted gaps in measurement and causal understanding. Future research should:

• Use objective devices (accelerometers, actigraphy) and direct fitness tests when possible to validate self‑reports.
• Disaggregate screen time into passive vs interactive and educational vs recreational categories.
• Measure sleep quality, variability and circadian timing in addition to duration.
• Employ longitudinal designs to trace recovery trajectories and identify critical windows for intervention.
• Test multicomponent interventions in pragmatic trials that evaluate combined effects on fitness, mental health and educational outcomes.
• Investigate structural determinants (housing density, access to outdoor space, school policies) that moderate behavior–fitness links and drive equity.

Practical steps for parents, educators and health professionals

For parents

• Aim for daily opportunities for at least 60 minutes of moderate‑to‑vigorous activity for school‑age children; model active habits and organize family movement routines.
• Move screens out of bedrooms; establish consistent bed and wake times even during out‑of‑school periods.
• Prioritize active alternatives when screen use is recreational: short walks, active games, bike rides.

For schools and educators

• Restore comprehensive physical education and recess where possible; if full PE is delayed, integrate short activity breaks and structured movement into classroom time.
• Coordinate with families to support consistent sleep schedules and reduce homework overload that extends screen use late into the evening.
• Provide access to guidance and resources for home‑based physical activity when in‑person classes are disrupted.

For health professionals

• Screen adolescents for low activity and high screen exposure during routine visits; offer tailored counseling and community resources.
• Advocate for policies that protect opportunities for youth movement during public health emergencies, including safe outdoor access and prioritized school reopening for physical education.

Final takeaways

Physical activity stood out as the primary 24‑hour behavior linked to perceived fitness among Ibero‑American youth during the early COVID‑19 pandemic. Insufficient activity was associated with poorer general, cardiorespiratory, muscular and motor fitness, with associations growing stronger in older children and adolescents. Screen time and sleep showed less consistent independent associations; however, failing multiple movement recommendations compounded risks, especially for adolescents. Restoring and prioritizing daily opportunities for moderate‑to‑vigorous activity should sit at the center of recovery plans, complemented by nuanced approaches to screen use and strategies to stabilize sleep.

FAQ

Q: What are the 24‑hour movement behavior (24HMB) recommendations used in this study? A: The study applied age‑specific guidelines: for physical activity, at least 60 minutes of moderate‑to‑vigorous activity daily (all seven days) for children and adolescents; screen time recommendations used WHO guidance (≤1 h/day for preschoolers) and Canadian guidance (≤2 h/day for children and adolescents); sleep recommendations followed age ranges such as 10–13 hours for 3–4‑year‑olds, 9–11 hours for 5–13‑year‑olds, and 8–10 hours for 14–17‑year‑olds.

Q: How was physical fitness measured? A: Fitness was measured using the International Fitness Scale (IFIS), a validated five‑item parent‑reported instrument assessing perceived general fitness, cardiorespiratory fitness, muscular fitness, speed/agility and flexibility. Responses were dichotomized into high (good/very good) versus not high (medium/poor/very poor).

Q: Can these findings tell us that low activity caused poor fitness? A: The study is cross‑sectional, capturing behavior and perceived fitness at the same time during early pandemic restrictions. Associations are robust but cannot establish causality. Longitudinal or intervention studies are needed to demonstrate causal effects.

Q: Why did screen time show weak or inconsistent associations with fitness? A: Nearly all participants exceeded recreational screen time recommendations, producing low variability and diminishing statistical power to detect effects. Screen time is heterogeneous — passive viewing differs from movement‑based gaming — and the measure did not distinguish type or timing. Additionally, screen time’s impact may be mediated through reductions in physical activity or disrupted sleep.

Q: How do country differences affect interpretation? A: Most variance in fitness outcomes was at the individual level, but some country‑level differences mattered (e.g., muscular fitness in children). National public‑health measures, the timing and extent of school closures, access to outdoor spaces and socioeconomic factors can shape behavior and therefore fitness outcomes. Interventions must be adapted to local conditions.

Q: What practical steps should schools take if another disruption occurs? A: Prioritize maintaining daily activity through creative, low‑contact strategies: structured home‑based PE modules, short outdoor sessions with distancing, active breaks during remote learning, provision of equipment loans for home use, and messaging to parents about maintaining routines for play and sleep.

Q: Who should be targeted first for recovery efforts? A: Adolescents and school‑aged children warrant prioritized attention because associations between insufficient activity and multiple fitness domains were strongest in these groups. Interventions should target both activity opportunities and combined behavior packages (activity, sleep, screen habits).

Q: Are there equity concerns in applying these findings? A: Yes. Access to private outdoor space, safe neighborhood infrastructure and institutional supports varies. Policy responses should include free or subsidized programming, safe public spaces, and school‑based delivery to reach children from lower‑resource households.

Q: What research is needed next? A: Longitudinal and experimental studies using objective measures of movement, detailed characterization of screen behaviors and sleep quality, and trials of multi‑behavior interventions will clarify causal pathways and effective public‑health strategies.

Q: Where can I find the original study details? A: The study appeared in Frontiers in Public Health (2026) and analyzed data collected in 2020 from Spain, Brazil and Uruguay. It reports comprehensive odds ratios, country‑level variance estimates and age‑stratified results for each fitness domain.