Recent advances in wearable technologies, digital health solutions, biomechanical monitoring systems, and artificial intelligence (AI) have transformed the way human performance, fatigue, and recovery are assessed across sport, exercise, and health settings. The increasing availability of real-time physiological, biomechanical, behavioral, and environmental data has created unprecedented opportunities to better understand human responses to training, competition, physical activity, rehabilitation, and daily life demands.

Despite these technological developments, significant challenges remain regarding data integration, interpretation, validity, reliability, and practical application. Researchers and practitioners are increasingly seeking evidence-based approaches that combine traditional monitoring methods with emerging technologies, including wearable sensors, machine learning algorithms, predictive analytics, digital biomarkers, and AI-driven decision-support systems.

This collection aims to bring together high-quality research that advances the monitoring and understanding of human performance, fatigue, recovery, and adaptation in sport, exercise, and health contexts. We welcome original research articles, systematic reviews, meta-analyses, methodological papers, and applied studies that explore innovative monitoring approaches and their practical implications.

Potential topics include, but are not limited to:

• Wearable technologies for monitoring physiological and biomechanical variables;
• Fatigue assessment and management in sport and exercise;
• Recovery monitoring and optimization strategies;
• Artificial intelligence and machine learning applications in performance analysis;
• Digital biomarkers and health monitoring systems;
• Training load monitoring and athlete management;
• Remote monitoring and telehealth approaches;
• Validation and reliability of monitoring technologies;
• Data integration from multiple monitoring systems;
• Human performance analytics in clinical, occupational, and sporting populations;

By bridging sport science, exercise physiology, biomechanics, health sciences, and data science, this collection seeks to provide new insights into how technology-driven monitoring can support performance enhancement, health promotion, injury prevention, recovery optimization, and evidence-based decision-making.

Social connection is increasingly recognized as a vital component of population health. It includes the structure of social networks, the support that flows through relationships, and the quality of interactions that contribute to well-being. Research across multiple disciplines has established that limited or poor social connection is linked to higher risks of premature mortality, mental health problems, cardiovascular disease, cognitive decline, lower educational attainment, and reduced economic participation. These findings highlight the need for rigorous and comparable metrics that can illuminate the distribution, determinants, and consequences of social disconnection at scale.

Despite a growing interest in social connection, population level measurement remains uneven. Studies often rely on single items, short scales, or constructs that capture only one aspect of the broader social landscape. Loneliness, social isolation, and social support are frequently treated as interchangeable, even though each reflects a different dimension of social life and may influence health through distinct pathways. In many settings, there is no routine surveillance of social connection. As a result, policymakers and practitioners lack the data needed to design effective interventions or monitor changes over time.

Conceptual clarity is a prerequisite for measurement. Social connection is multidimensional and includes the size and diversity of social networks, the availability of emotional or instrumental support, and the positive or negative qualities of relationships. Social isolation is the objective absence of social roles or interactions. Loneliness is the subjective experience of wanting more or better connection than one currently has. These concepts intersect but are not synonymous. Accurate assessment requires tools that reflect each dimension and that can be adapted or validated across cultures, languages, and life stages.

Research from low and middle income countries is particularly limited even though many of these settings experience social conditions that may heighten vulnerability to social disconnection. Large and rapid demographic transitions, urban migration, changing family structures, economic insecurity, and exposure to conflict or displacement can all shape patterns of social connection. Yet most validated instruments originate in high income countries, and many have not been adapted or tested for linguistic, cultural, and contextual relevance in LMIC populations.

The need for strong metrics extends beyond conceptual work. The use of digital technology, demographic change, shifting household structures, migration, and urbanization all influence how social connections form and function. These forces create new opportunities for measurement, such as digital interaction data or innovative mixed-method approaches, but they also introduce new challenges. Measures grounded in one cultural or linguistic context may not translate easily to another. Children, adolescents, older adults, and marginalized groups may require tailored instruments that reflect their social realities. Community and policy interventions also need evaluation frameworks that capture both intended and unintended effects on connection.

This special issue invites research that advances the science of measuring social connection for population health. Relevant contributions include but are not limited to:

1. New conceptual models and frameworks for assessing social connection at scale

2. Validation or adaptation of measurement instruments across cultures and population groups

3. Methods for distinguishing between loneliness, social isolation, and social support in surveillance systems

4. Epidemiologic analyses that examine levels, patterns, and trends in social connection

5. Studies linking social connection metrics with health, economic, or educational outcomes

6. Approaches to integrating social connection into existing population health surveys

7. Measurement of social connection in digital and hybrid social environments

8. Evaluation metrics for policies, community initiatives, and individual level interventions

9. Proposals for harmonized indicators that support international comparisons

All submissions in this collection undergo the journal’s standard peer review process. Similarly, all manuscripts authored by a Guest Editor(s) will be handled by the Editor-in-Chief. As an open access publication, this journal levies an article processing fee (details here). We recognize that many key stakeholders may not have access to such resources and are committed to supporting participation in this issue wherever resources are a barrier. For more information about what support may be available, please visit OA funding and support, or email OAfundingpolicy@springernature.com or the Editor-in-Chief.