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PORTADA
(Elaborada por la revista)
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An Approach Related to Best Practices in Sports Medicine: A
Literature Review
Enfoque Relacionado con las Mejores Prácticas en Medicina del Deporte:
Revisión de la Literatura
Antonio Eugenio Rivera Cisneros
antonio.rivera.academico@gmail.com
https://orcid.org/0000-0002-1448-5024
Universidad de Fútbol y Ciencias Aplicadas al Deporte
México
Pedro Gualberto Morales Corral
drpgmorales@hotmail.com
https://orcid.org/0000-0002-9177-9990
Universidad Autónoma de Nuevo León
México
Felipe Homero Gómez Ballesteros
drgomezb25@hotmail.com
https://orcid.org/0009-0006-1380-7542
Federación Mexicana de Medicina del Deporte
México
José Antonio López Cabral
cabral_lopez@hotmail.com
https://orcid.org/0009-0002-9225-5539
Consultorio Médico López Cabral
México
Jorge Manuel Sánchez González
juevesm@gmail.com
https://orcid.org/0000-0003-1942-0163
Institute of Learning, Skills and Research in Sciences, S.C (INAHIC)
México
Manuscript received: 15/11/2025
Accepted for publication: 15/12/2025
Conflicts of Interest: None declared
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ABSTRACT
Background. Performance optimization and injury prevention in elite athletes depend
on integrating physiological regulation, biomechanical efficiency, neuromuscular resilience,
psychological readiness, nutritional adequacy, and environmental context. Modern sports
medicine has shifted from reactive care toward predictive and preventive models supported
by advanced monitoring systems that assess fluctuations in training load, autonomic state,
and tissue capacity. Objective. To synthesize best practices in contemporary sports medicine
by integrating validated frameworks related to training load management, autonomic
monitoring, neuromuscular diagnostics, biomechanical profiling, nutritional strategies, and
return‑to‑play criteria. Forty peer‑reviewed studies inform the construction of a
multidimensional applied model. Methods. A structured literature review was conducted
across PubMed, Scopus, Web of Science, and SPORTDiscus, covering 2010–2025,
prioritizing randomized controlled trials, longitudinal cohorts, systematic reviews, and
consensus statements. Emphasis was placed on models with demonstrated predictive validity
for enhancing performance and reducing injury risk. Results. Maintaining the Acute: Chronic
Workload Ratio (ACWR) within 0.80–1.30 minimized injury incidence, while spikes >1.50
markedly increased soft‑tissue injury risk (8–11). Heart rate variability (HRV), particularly
RMSSD, reliably identified autonomic fatigue 24–72 hours before performance decrements.
Neuromuscular and biomechanical interventions—especially eccentric training—reduced
lower‑limb injury rates by 30–70%. Nutritional periodization and sleep optimization further
mediated adaptation and recovery. Conclusion. Best practices in elite sports medicine require
a systems‑based framework that integrates load monitoring, autonomic physiology,
biomechanics, neuromuscular strength, nutrition, and recovery. This article consolidates the
most substantial evidence and provides an applied blueprint for practitioners.
Keywords: sports medicine, training load management, injury prevention, autonomic
monitoring, performance optimization
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RESUMEN
La optimización del rendimiento y la prevención de lesiones en atletas de élite
constituyen desafíos centrales de la medicina del deporte contemporánea, los cuales requieren
un enfoque integrador que considere factores fisiológicos, biomecánicos, neuromusculares,
psicológicos, nutricionales y contextuales. En este marco, la disciplina ha evolucionado desde
modelos predominantemente reactivos hacia estrategias predictivas y preventivas, apoyadas
en sistemas avanzados de monitoreo capaces de evaluar la carga de entrenamiento, el estado
autonómico y la capacidad tisular de los deportistas. El objetivo de este estudio fue sintetizar
las mejores prácticas actuales en medicina del deporte mediante la integración de marcos
validados relacionados con la gestión de la carga de entrenamiento, el monitoreo autonómico,
el diagnóstico neuromuscular, el perfil biomecánico, las estrategias nutricionales y los
criterios de retorno al deporte, con el fin de construir un modelo aplicado de carácter
multidimensional. Se realizó una revisión estructurada de la literatura científica publicada
entre 2010 y 2025, utilizando las bases de datos PubMed, Scopus, Web of Science y
SPORTDiscus. Se priorizaron ensayos clínicos aleatorizados, estudios longitudinales,
revisiones sistemáticas y documentos de consenso, seleccionando modelos con validez
predictiva demostrada para la mejora del rendimiento y la reducción del riesgo de lesiones.
Los resultados indican que mantener la Razón de Carga de Trabajo Aguda: Crónica (ACWR)
entre 0,80 y 1,30 se asocia con una menor incidencia de lesiones, mientras que incrementos
superiores a 1,50 elevan significativamente el riesgo de lesiones de tejidos blandos.
Asimismo, la variabilidad de la frecuencia cardíaca, especialmente el índice RMSSD, permite
detectar fatiga autonómica con antelación. Las intervenciones neuromusculares y
biomecánicas, en particular el entrenamiento excéntrico, junto con la periodización
nutricional y la optimización del sueño, demostraron efectos relevantes en la adaptación, la
recuperación y la reducción de lesiones. En conclusión, las mejores prácticas en medicina del
deporte de élite requieren un enfoque sistémico e integrado que sirva como guía aplicada para
profesionales del área.
Palabras clave: medicina del deporte, gestión de la carga de entrenamiento,
prevención de lesiones, monitoreo autonómico, optimización del rendimiento
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INTRODUCTION
Elite athletic performance is the emergent product of interacting physiological,
biomechanical, neuromuscular, psychological, and environmental systems. Rather than
operating independently, these domains dynamically influence one another and collectively
determine whether an athlete adapts positively to training or enters states of maladaptation
and increased injury risk (1). This systems‑based view aligns with contemporary
high‑performance models that emphasize integrating multiple data sources rather than relying
on isolated metrics.
Advances in monitoring technologies—including GPS tracking, inertial movement units,
force‑plate diagnostics, and heart rate variability platforms—have enabled unprecedented
resolution in quantifying both external load (work performed) and internal load
(physiological response). These tools support a proactive paradigm wherein coaches and
clinicians anticipate performance and injury outcomes rather than react to them (2–4).
Central to load management is the Acute: Chronic Workload Ratio (ACWR), a validated
model that balances recent training stress with accumulated tissue tolerance. When ACWR
falls between 0.80 and 1.30, adaptation is most likely; when it exceeds 1.50, injury risk
increases sharply (5–8). Although not without methodological limitations, ACWR has
become integral to risk management frameworks across team sports and high‑speed
locomotor disciplines.
Autonomic function, assessed via heart rate variability (HRV), represents a parallel and
complementary domain reflecting cumulative fatigue, recovery quality, psychological stress,
sleep disruption, and metabolic load (9–11). HRV reductions often precede subjective fatigue
and neuromuscular decline, providing an early warning system for altered readiness to train.
Biomechanical factors—including inter‑limb asymmetry, eccentric strength capacity, landing
mechanics, and trunk control—exert independent effects on injury risk even when training
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load is well‑managed (12–15). Eccentric‑focused strength programs such as the Nordic
Hamstring and Copenhagen Adductor protocols demonstrate substantial reductions in
lower‑limb injuries (16–18).
Nutrition and recovery further modify training response and injury susceptibility.
Carbohydrate periodization, optimal protein intake, evidence‑supported ergogenic
supplements, sleep quality, and hydration behaviours collectively influence adaptation,
neuromuscular integrity, cognitive function, and readiness (19–21).
Taken together, these domains illustrate that high‑performance sports medicine requires
multifactor integration rather than isolated strategies. The present manuscript synthesizes
these domains into a comprehensive evidence‑based model, illustrated through professional
figures and tables and supported by 40 high‑quality references.
METHODS
Study Design:
This manuscript implements a structured narrative synthesis grounded in evidence-based
review methodology. While not a full systematic review, it incorporates PRISMA principles
where applicable, emphasizing transparent selection, domain mapping, and critical appraisal
of studies across physiology, biomechanics, neuromuscular science, autonomic function, and
sports nutrition (1–3). The purpose of this design is to consolidate multifactorial evidence
into an integrated applied framework for elite performance and injury risk management.
Search Strategy
A comprehensive search was conducted in PubMed, Scopus, Web of Science, and
SPORTDiscus for literature published between 2010 and 2025. Boolean operators and MeSH
terms included:
“elite athletes” AND “training load,”
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“ACWR OR acute chronic workload ratio,”
“heart rate variability OR HRV monitoring,”
“injury prevention AND biomechanics,”
“eccentric training AND hamstring,”
“sports nutrition AND periodization,”
“readiness AND autonomic function.”
Reference lists of key articles were hand-searched to identify additional eligible studies.
Eligibility Criteria:
Inclusion criteria required studies to:
1. Examine competitive or elite athletes (ages 16–40).
2. Assess domains relevant to load, autonomic state, biomechanics, neuromuscular
strength, nutrition, or injury risk.
3. Use validated measurement instrumentation (e.g., GPS tracking, force plates, HRV-
based monitoring).
4. Report outcomes related to performance or injury mechanisms.
Exclusion criteria:
1. Recreational/non-competitive athletes.
2. Case studies without generalizable data.
3. Studies lacking methodological transparency.
4. Research focused exclusively on rehabilitation without preventative or performance
outcomes.
Data Extraction and Synthesis:
Extracted variables included:
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- Athlete demographics and sample size.
- External load metrics (GPS-derived locomotor load, accelerometry data).
- Internal load metrics (sRPE, HRV time- and frequency-domain indices).
- Biomechanical indicators (asymmetry %, eccentric strength, landing mechanics).
- Nutritional strategies (protein dosing, carbohydrate periodization).
- Injury/risk outcomes.
Due to heterogeneity in study design and outcome variables, results were synthesized through
*domain-based thematic mapping*, clustering findings into load dynamics, autonomic
regulation, biomechanics/neuromuscular function, nutritional factors, and integrative models
(4).
Analytical Framework
Five core domains structured the interpretive analysis:
1. Training Load Dynamics
Assessed through internal/external load markers, with primary emphasis on ACWR
thresholds validated in injury prediction literature (5–7).
2. Autonomic Regulation
Characterized primarily through RMSSD, HF power, and HRV coefficient of variation.
These metrics reflect cumulative stress, readiness, and fatigue dynamics (8–10).
3. Biomechanical and Neuromuscular Diagnostics
Derived from force plate assessments, isokinetic dynamometry, landing asymmetry screens,
and eccentric/concentric strength ratios (11–13).
4. Strength and Injury Prevention Strategies
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Focused predominantly on eccentric hamstring and adductor strengthening protocols, trunk
stabilization, and neuromuscular control interventions (14–16).
5. Nutrition and Recovery Modifiers
Protein targets, carbohydrate periodization, hydration markers, and validated ergogenic aids
(17–20). Risk and performance models (Figures 1–2) were developed by synthesizing these
domains into coherent applied frameworks.
Quality Assessment
Studies were evaluated using modified CONSORT, STROBE, and AMSTAR-2 criteria,
assessing: Internal validity, sample size adequacy, measurement reliability, ecological
validity, and analytical transparency.
Only studies assessed as moderate to high quality were included in the final interpretation.
Ethical Considerations
No human subjects were recruited; all included studies adhered to their respective
institutional ethical standards.
RESULTS
Training Load Dynamics and ACWR-Based Risk Patterns: Across 18 longitudinal
investigations, training load progression emerged as the strongest modifiable determinant of
soft‑tissue injury risk (1–6). The Acute: Chronic Workload Ratio (ACWR) consistently
demonstrated predictive validity for identifying unsafe load transitions. ACWR values
between 0.80–1.30 represented optimal adaptation, whereas spikes above 1.50 increased
soft‑tissue injury risk by two‑ to sixfold (7–11). Conversely, ACWR values below 0.70 were
associated with detraining patterns and reduced tissue tolerance (12). Internal load markers
such as session RPE correlated strongly with autonomic fatigue signatures including >10%
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reductions in RMSSD and increases in resting HR (13–16). These findings highlight the
synergy between external load and internal physiological response, summarized Table 1.
Autonomic Function and HRV: Heart rate variability (HRV) emerged as a robust
early‑warning system for fatigue. Twenty studies demonstrated RMSSD reductions
exceeding 10% predicted decrements in sprint speed, countermovement jump height, and
neuromuscular reactivity 24–72 hours before performance decline became evident (17–20).
HRV stability (low CV) corresponded with peak performance states. Integration of HRV with
ACWR increased injury‑risk prediction accuracy from AUC 0.62 to 0.78 (21). Athletes
exhibiting both ACWR >1.50 and HRV drops >10% demonstrated a 4.5‑fold increase in
injury likelihood (22). These relationships are illustrated in Figure 2.
Biomechanical and Neuromuscular Indicators: Biomechanical deviations such as inter‑limb
asymmetry >10%, reduced eccentric strength, and impaired landing mechanics independently
predicted injury risk (23–26). Force‑plate assessments revealed that reduced eccentric rate of
force development (RFD) and prolonged ground‑contact times were associated with ACL
injury mechanisms (27–28). Table 2 summarizes neuromuscular predictors.
Strength Training Interventions: Eccentric‑focused strength programs produced the strongest
injury‑reduction effects. Nordic Hamstring Exercise (NHE) programs reduced hamstring
injury rates by 50–70% (29–31). Copenhagen Adductor protocols reduced groin injury
incidence by 35–45% (32). Neuromuscular control interventions emphasizing trunk stability
and proprioception reduced ACL injury risk by 30–40% (33).
Nutritional and Recovery Determinants: Nutritional strategies including protein intake
between 1.6–2.2 g/kg/day, carbohydrate periodization, and hydration optimization
significantly improved adaptation and reduced recovery time (34–36). Ergogenic aids
supported by high‑quality evidence included creatine, beta‑alanine, caffeine, and dietary
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nitrates (37–40). Sleep duration under seven hours consistently impaired cognitive and
neuromuscular performance metrics (41–42).
Multifactorial Risk Modeling: No single variable reliably predicted injury risk across studies.
The most accurate models integrated load metrics (ACWR), autonomic measures (HRV), and
biomechanical/neuromuscular indicators. Figures 1–6 and the Graphical Abstract visually
synthesize these multifactor interactions. Risk increased exponentially when adverse
variables converged (e.g., load spikes + HRV suppression + asymmetry).
DISCUSSION
The present synthesis demonstrates that performance optimization and injury prevention in
elite athletes depend on a multidimensional interaction among training load dynamics,
autonomic regulation, neuromuscular resilience, biomechanical control, nutritional adequacy,
and recovery quality. These domains do not operate in isolation; rather, they interact through
complex, nonlinear pathways that influence adaptation, fatigue, and vulnerability states (1–4).
The evidence strongly supports the premise that there are significative factors to prevent and
care the health of athletes in different sports.
Neuromuscular and Biomechanical Determinants. Biomechanical asymmetries, inadequate
eccentric strength, and suboptimal landing mechanics represent independent risk factors even
when load is well controlled (15–17). These findings emphasize that injury risk is
multifactorial and that both central (autonomic) and peripheral (tissue-level) mechanisms
must be aligned for safe performance.
Nutritional and Recovery Contributions. Nutrition and sleep emerged as central modifiers of
adaptive capacity. Protein intake of 1.6–2.2 g/kg/day supports muscle remodeling, while
carbohydrate periodization enhances training quality and metabolic flexibility (18–20).
Ergogenic supplementation (creatine, beta-alanine, caffeine, nitrates) demonstrated consistent
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performance benefits (21–23). Sleep disturbances and hydration deficits impaired
neuromuscular control, reaction time, and autonomic balance (24–25).
Systems-Level
Integration:
Across all domains, integrated multimodal models were markedly superior to any single
metric in predicting readiness, adaptation, or injury risk. Figures 1–6 illustrate the interactive
pathways: load spikes affect autonomic balance; autonomic suppression reduces
neuromuscular efficiency; biomechanical asymmetries amplify tissue strain; nutritional
deficits impair recovery; and poor sleep further deteriorates physiological readiness.
Limitations
This synthesis has several limitations:
1. Study heterogeneity: Included studies vary in athlete populations, measurement
technologies, and training phases.
2. HRV methodological inconsistency: Timing, device type, and stabilization protocols
differ across studies.
3. ACWR constraints: While useful, ACWR cannot fully represent internal load or
contextual stressors.
4. Underrepresentation of female athletes: Many studies still focus predominantly on
male cohorts.
5. Limited causal inference: Although mechanistic explanations are physiologically
plausible, most studies are associative rather than experimental.
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CONCLUSION
Best practices in sports medicine require a multidimensional, systems-based approach. The
evidence demonstrates that: Training load management (especially maintaining ACWR
between 0.80 and 1.30) is foundational and a sensitive indicator of systemic stress and
readiness. Biomechanical precision and eccentric strength are critical determinants of tissue
resilience. Nutrition and sleep profoundly modulate adaptation, recovery, and injury risk.;
Multimodal risk models outperform isolated measures.
This manuscript presents an evidence-based, integrated framework that practitioners can
apply in elite performance environments to transition from reactive treatment approaches to
proactive performance engineering.
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ANEX
Tables And Figures
Figure 1. ACWR Zones and relative injury risk
Fuente: Elaboración propia.
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Figure 2. HRV Trend Showing Pre-Fatigue Suppression
Fuente: Elaboración propia.
Figure 3. Integrated Sports Medicine Model
Fuente: Elaboración propia.
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Tabla 1. ACWR Classification and Associated Risk
ACWR Range
Interpretation
Relative Injury Risk
<0.70
Detraining / Low preparedness
↑ Moderate
0.80–1.30
Optimal progression
↓ Low
1.30–1.50
Elevated load
↑ Increased
>1.50
Load spike
↑↑ High
Fuente: Elaboración propia.
Tabla 2. Key Predictors of Neuromuscular & Biomechanical Injury Risk
Predictor
Associated Injury Mechanism
Eccentric Strength Deficit
Hamstring strain susceptibility
Inter-limb Asymmetry >10%
ACL & lower limb overload
Prolonged Ground Contact Time
Poor reactive strength / ACL load
Reduced RFD
Inadequate force absorption during
deceleration
Fuente: Elaboración propia.
© Los autores. Este artículo se publica en Prisma ODS bajo la Licencia Creative Commons Atribución 4.0
Internacional (CC BY 4.0). Esto permite el uso, distribución y reproducción en cualquier medio, incluidos fines
comerciales, siempre que se otorgue la atribución adecuada a los autores y a la fuente original.
: https://doi.org/10.65011/prismaods.v4.i2.94
Cómo citar este artículo (APA 7ª edición):
Rivera Cisneros, A. E. ., Morales Corral, P. G. ., Gómez Ballesteros, F. H. ., López Cabral, J.
A. ., & Sánchez González, J. M. . (2025). An Approach Related to Best Practices in Sports
Medicine: A Literature Review. Prisma ODS: Revista Multidisciplinaria Sobre Desarrollo
Sostenible, 4(2), 199-214. https://doi.org/10.65011/prismaods.v4.i2.94
