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other·sports medicine, exercise physiology, public health, epidemiology·PMC10062547

InTrack project ˗ Theoretical framework, design, and methods: A study protocol

PLOS ONE · 8 authors, 11 centres

AI SUMMARY

FIDELITY 100%

POPULATIONRunners, of both sexes, aged ≥18 years, from four countries, who self-classify as a runner and have taken part in at least one official competition in the last 12 months

INTERVENTIONNot applicable (observational cross-sectional study; no intervention)

COMPARISONBetween-country comparisons based on micro-level (training, sociodemographic), meso-level (sports programs, clubs), and macro-level (HDI, GDP, cultural dimensions) variables

This summary was generated by AI from a single paper. It has not been reviewed by a clinician and is not clinical advice. Verify against the source before acting on it.

AGENT 01 · STRUCTURED SUMMARY

TRACE TO SOURCE

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TL;DR

This study protocol describes the InTrack Project, a cross-sectional and cross-cultural investigation designed to examine how micro-level (individual athlete characteristics), meso-level (sports organizations), and macro-level (country demographics and economics) factors explain variance in running performance within and between countries. The project will recruit runners from four countries and use multilevel analysis, latent class analysis, and network models to test cross-level interactions. The findings aim to identify environmental and policy variables that predict performance differences, providing evidence to inform sports development programs.

Full summary

5,390 CHARS

**Background:** Athletes’ performance is a dynamic, non-linear, and multidimensional phenotype that results from complex interactions between individual and environmental factors. The ecological systems theory (Bronfenbrenner, 1977) provides a framework for understanding these interactions across hierarchical levels: the micro-level (intrapersonal and training characteristics, coach-athlete dyad, family support), meso-level (sports clubs, federations), and macro-level (culture, economy, demographic indicators). Previous research has shown that population size, Human Development Index (HDI), and political systems can explain up to half of the performance variance between countries. For example, athletes from cities with high HDI were six times more likely to become elite swimmers compared to those from lower HDI cities. In running, the 20 best sprinters worldwide (2006–2016) came from high-HDI countries, while endurance event specialists (10,000 m or above) predominantly came from low/middle-HDI countries, partly reflecting the African phenomenon. Despite growing interest, research on country-level variables and cross-level interactions in running performance remains limited. The InTrack Project was designed to address this gap.

**Methods:** This is a cross-sectional and cross-cultural study. The sample will comprise runners of both sexes from four countries. The expected sample size is at least 80 participants per country (estimated using Gpower 3.1: effect size 0.35; α err prob 0.05; Power 0.95; number of predictors 10). Ethical approval was obtained in Brazil (Federal University of Sergipe, protocol 5.286.914), Kenya (NACOSTI/P/22/18904), and Portugal (CEFADE 12/2022). Eligibility criteria: runners aged ≥18 years, self-classified as a runner, participation in at least one official competition in the last 12 months, and completion of the online questionnaire. Countries are included only after local ethics committee approval. Data collection occurs entirely in a virtual environment. Recruitment is conducted through athletics federations, higher education institutions, social media, personal networks, and sports clubs (2022/2023). After providing informed consent via a web survey, participants complete a 37-item questionnaire (approximately 15 minutes) covering: identification (age, sex); anthropometrics (body height, body mass); sociodemographic profile (country of residence, monthly income, educational level, marital status); training characteristics (volume, duration, frequency/week, sessions/day, practice time, running pace); involvement in official running events; motivation; relationship with coaches; and perception of contextual support (family, friends, coach, training facilities). Mandatory questions include country of residence and running pace (outcome variable). The questionnaire’s psychometric quality will be tested through expert evaluation and a pilot test among Brazilian runners, assessing content, objectivity, clarity, readability, and understanding. Translation and back-translation will be performed for non-Portuguese speakers. Country-level (meso-level) information will be obtained from free-access web pages and documents, including existence of talent development programs, number of high-performance athletes in national/international rankings, number of sports clubs, number of high-performance competitions, and sports investment. Macro-level information includes population size and density, HDI, gross domestic product, per capita income, annual sports investment, number of high-performance athletes internationally, and cultural dimensions (Hofstede’s six dimensions: power distance, individualism, masculinity, uncertainty avoidance, long-term orientation, indulgence). Expected statistical procedures: descriptive statistics (five-number summary, mean/SD, frequencies); multilevel analysis (hierarchical linear models) with intraclass correlation coefficient to quantify variance attributable to country-level grouping; two-level models (level 1: runners; level 2: countries); Latent Class Analysis (LCA) to cluster subgroups based on economic aspects, training characteristics, and environmental perception; Network analysis to examine non-linear interactions and identify hub variables bridging micro and macro levels; and multiple regression models testing additive and multiplicative effects. Confidence intervals will be fixed at 95%. Results will be disseminated via peer-reviewed journals and scientific events, following the Checklist for Reporting Results of Internet E-Surveys (CHERRIES).

**Key Results:** This is a study protocol; no results are reported. The paper describes the planned methodology and statistical approaches.

**Clinical Implications:** Although this is not a clinical intervention study, the findings are expected to provide scientific support for public policies and sports development programs by identifying environmental characteristics (at micro, meso, and macro levels) that predict runners’ performance within and between countries. Understanding cross-level interactions may help optimize resource allocation, talent identification, and training environment design. The project also aims to fill gaps regarding variables that connect different levels of information, potentially informing strategies to improve both individual and societal outcomes through sport.

PICO

PPOPULATION

Runners, of both sexes, aged ≥18 years, from four countries, who self-classify as a runner and have taken part in at least one official competition in the last 12 months

IINTERVENTION

Not applicable (observational cross-sectional study; no intervention)

OOUTCOME

Running performance (measured as running pace, self-reported via mandatory questionnaire item)

STUDY TYPE

other

SPECIALTY

sports medicine

SUMMARISED BY

AI pipeline

FIDELITY CHECK

100% · A

ORIGINAL PAPER

PMC10062547

InTrack project ˗ Theoretical framework, design, and methods: A study protocol

PLOS ONE

READ THE FULL PAPER

ABSTRACT

Abstract Sports performance is the result of a complex interaction between individual and environmental factors. The purpose of this paper is to explain the methods used in the InTrack Project, a cross-sectional and cross-cultural project developed to investigate the variance in the performance of runners from different countries and to understand whether the differences in the performance can be explained by micro-level (athletes characteristics and proximal environment), meso-level (the distal environment that plays a relevant role on the relationships established at micro-level), and the macro-level (environmental features that shape countries characteristics). The sample will be comprised of runners, of both sexes, from four countries. Data collection will be performed in two steps: i) Individual information and ii) Country-level information. At the individual level, data will be obtained from an online survey. At the country level, characteristics data will be obtained from the secondary data available (demographic, social, and economic variables). Statistical procedures expected to be used include multilevel analysis, latent class analysis, addictive and multiplicative interaction in regression models. This wealth of information is of relevance to fill gaps regarding the existence of variables to connect different levels of information, and to provide scientific support about environmental characteristics important to predict runners’ performance within and between countries.

CITATION EXPORT

Mabliny Thuany, Thayse Natacha Gomes, Katja Weiss et al.. InTrack project ˗ Theoretical framework, design, and methods: A study protocol. PLOS ONE. (2023). https://doi.org/10.1371/journal.pone.0283157

AUTHORS

8 · 11 CENTRES

Mabliny Thuany, Thayse Natacha Gomes, Katja Weiss, Volker Scheer, Lee Hill, Ramiro Rolim, Beat Knechtle, Marcos André Moura dos Santos

1Centre of Research, Education, Innovation and Intervention in Sport (CIFI2D), Faculty of Sport, University of Porto, Porto, Portugal
2Department of Physical Education, Post-Graduation Program of Physical Education, Federal University of Sergipe, São Cristóvão, SE, Brazil
3Department of Physical Education & Sports Science, University of Limerick, Limerick, Ireland
4Physical Activity for Health Cluster, Health Research Institute, University of Limerick, Limerick, Ireland
5Medbase St. Gallen Am Vadianplatz, St. Gallen, Switzerland
6Institute of Primary Care, University of Zurich, Zurich, Switzerland
7Ultra Sports Science Foundation, Pierre-Benite, France
8Division of Gastroenterology & Nutrition, Department of Pediatrics, McMaster University, Hamilton, Canada
9Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
10Associated Postgraduate Program in Physical Education, University of Pernambuco and Federal University of Paraiba, Recife, Brazil
11University of Split, CROATIA