Effects of Polarized and Threshold Intensity Distribution Models on Race Time and Body Composition in Recreational Runners Aged 20-45 Years: a Randomized Controlled Trial
Phase: NA
Conditions: Physical Activity, Physical Activity Intensity, Physical Activity in Adults, Running, Running Performance, Running Endurance
Sponsor: Universidad Pedagogica Nacional, Colombia

NATIONAL PEDAGOGICAL UNIVERSITY  
 
   
MASTER'S DEGREE IN SPORTS SCIENCE AND PHYSICAL ACTIVITY  
 
 
 
  
THESIS  
   
PRIOR TO OBTAINING THE TITLE OF  
Master of Science in Sport and Physical Activity  
   
 
ISSUE  
 
Effects of Polarized and Threshold Intensity Distribution Models on Running Time and 
Body Composition in Recreational Runners Aged 20 to 45 Years: A Randomized 
Controlled Trial  
 
 
Author(s):  
Angie Tatiana Joya Medina  
Jesús Giuliani Martínez Muñoz  
Julian David Suarez Mora  
  
 
 
 
 
Tutor:  
Dr. Eduar Alonso Ceballos Bernal  
  
 
 
NCT Number: 
Pending 
 
 
 
 
 
 
COLOMBIA  
Document date  
March 21, 2026 
 

 
 
Primary Objective  
  
To compare the effects of polarized and threshold TID models over 12 weeks on 5 km 
running time in recreational runners aged 20-45 years.  
 
Secondary Objective  
  
To compare the effects of polarized and threshold TID models over 12 weeks on body composition 
in recreational runners aged 20-45 years.  
  
Specific Objectives  
  
• 
Characterize the sample of recreational runners between 20 and 45 years old by the 
initial assessment of 5 km running time and body composition before the intervention.  
• 
Apply polarized and threshold intensity distribution models (IDM) as intervention 
strategies in the assigned groups for 12 weeks.  
• 
Evaluate the changes in 5 km running time and body composition between the 
groups subjected to the polarized DIE model and the threshold TID model after 12 
weeks of intervention.  
 
Study Assumptions  
 
Primary Alternative Hypothesis  
  
The polarized TID model is superior by at least 60 seconds in 5k running time, compared 
to the threshold TID in a 12-week program in recreational distance runners.  
 
Secondary Alternative Hypothesis  
  
The polarized TID model is superior in body composition compared to the threshold TID 
model in a 12-week program in recreational distance runners.  
 
Primary Null Hypothesis 
  
  
No differences in 5k running time were identified between the polarized TID model 
compared to the threshold TID model in a 12-week program in recreational distance runners.  
  
Secondary Null Hypothesis  
  
No differences in body composition were identified between the polarized TID model 
compared to the threshold TID model in a 12-week program in recreational long-distance runners.  
 
 

 
 
Methodology  
Variables  
 
Two variables were considered: dependent and independent. The dependent variables 
included running time, measured as total time in minutes/seconds, and body composition, 
measured as body fat percentage, muscle mass percentage, and body mass index (BMI kg/m²). 
These variables were affected by the independent variables, which were the polarized and 
threshold DIE models (see Table 1). As Gil Pascual (2015) argues, there was one dependent 
categorical variable, with as many categories as there were groups, and a set of discriminant 
independent variables, in which the different groups were assumed to differ.  
 
Operationalization of Variables  
  
Table 3. Operationalization of the variables.  
  
Variable  
Nature  
According to 
its 
continuity  
Values or 
unit of 
measurement  
Function in the 
study  
Verification 
method  
Age  
Quantitative  
( For reason)  
Continue  
Years  
Independent or 
confounding 
variable 
(depending on 
the design)  
Citizenship 
card  
Gender  
Qualitative  
Nominal  
  
Discrete 
(nominal)  Male / Female  Confusing or 
controlling  
Citizenship 
card  
5K race time  
Quantitative  
That makes 
sense  
Continue  
Minutes and 
seconds (e.g., 
25:45)  
Dependent  
5K Test  
Body 
composition  
Quantitative 
That makes 
sense  
Continue  
% body fat,  
% muscle 
mass,  
body mass 
index (BMI 
kg/m²)  
Dependent or 
confusing  
Bioimpedance 
scale  
Muscle mass  
Quantitative  
That makes 
sense  
Continue  
Kilograms or 
percentage 
(%)  
Dependent  
Bioimpedance 
scale  
Source: own elaboration.  
  
  
 

 
 
Research Approach  
  
Regarding the research approach, the research idea, objectives, and variables to be 
investigated are considered. The approach adapted to this project is quantitative. As Gil Pascual 
(2015) states, the quantitative approach in the research process, in most cases, aims to generalize 
results to a target population using data from a sample randomly drawn or selected from that 
population. In this process, statistical hypotheses about population parameters are formulated 
based on knowledge of the sample. The researcher must distinguish between research hypotheses, 
formulated as conjectures about a topic of scientific interest, and statistical hypotheses, formulated 
in statistical terms of probability about one or more population parameters. Examples of these 
hypotheses can be found in educational research when decisions are being made about the 
effectiveness of one teaching method compared to another, or when assessing whether certain 
relationships between variables observed in a sample also exist in the population. These are just 
two examples, among countless others, where hypothesis testing must be performed.  
 
Type of Research  
  
Two-arm parallel randomized controlled clinical trial (RCT) designed with the CERT and 
CONSORT guidelines, Friedman, Furberg, DeMets, Reboussin and Granger (2015). 
 
Furthermore, in relation to the research approach and in accordance with the objectives and 
variables to be investigated, the design type is experimental, as stated by Ñaupas Paitán et al. 
(2018). Experimental design allows for establishing the consequences achieved after applying an 
experiment. In this method, the researcher controls the independent variable, which is generally a 
treatment, applies it to the dependent variable, and explains what has happened.  
 
Experiments manipulate treatments, stimuli, influences, or interventions (called 
independent variables) to observe their effects on other variables (dependent variables) in a 
controlled situation. In other words, experimental designs are used when the researcher intends to 
establish the possible effect of a manipulated cause.  
 
In this research, the aim is to determine the effects of polarized, and threshold DIE intensity 
distribution models on running time in recreational runners between 20 and 45 years old.  
 
Scope of the Investigation  
  
In accordance with the complexity of the research objectives, its scope is explanatory, as 
supported by Ñaupas Paitán et al., 2018. Explanatory studies are based on properly formulated 
problems that seek to establish cause-and-effect relationships. They necessarily work with 
hypotheses that explain the effect of independent variables on the dependent variable. This is a 
more complex, deeper, and more rigorous level of basic research, whose main objective is the 
verification of causal or explanatory hypotheses; the discovery of new socio-scientific laws, of 
new social micro-theories that explain the causal relationships of the properties or dimensions of 
facts, events within the system, and social processes. They work with causal hypotheses, meaning 
they explain the causes of facts, phenomena, events, and natural or social processes. At this level 

of research, the formulation of hypotheses is fundamental because they guide the research path. 
Investigating without hypotheses is like walking in the jungle or sailing on an ocean without a 
compass, as we will see later when analyzing the importance of hypotheses.  
 
 
 
Inclusion and Exclusion Criteria  
 
Table 4. Inclusion and exclusion criteria 
 
Inclusion  
Exclusion  
 
  
• 
Age between 20 and 45 years  
• 
1 to 3 years of experience  
• 
Men's race time between 25:00 
and 40:00  
• 
Women's race times between 
28:00 and 43:00  
• 
Residing in Bogotá or 
surrounding municipalities at an 
altitude of 2,640 meters above sea 
level  
• 
Have a GPS system  
• 
Having a chronic non-
communicable disease  
• 
Smokers or drinkers  
• 
Having some musculoskeletal 
injury.  
• 
DO NOT sign the informed 
consent form  
• 
10% compliance with training 
sessions.  
Source: own elaboration.  
  
  
Types of Sampling  
  
For selecting the type of sample, the type of design must be considered, which in this case 
is experimental. Therefore, the appropriate approach is probabilistic sampling. According to 
Ñaupas Paitán et al. (2018), this is based on the law of large numbers and probability calculations 
(Ander-Egg, 1995). From these two laws, those related to the sampling procedure are derived. 
These are: Law of statistical regularity. This implies that the units of a sample (n) have the same 
characteristics as the population (N) to which they belong. Law of inertia of large numbers. 
According to this law, the result obtained in a sample (n) is almost always the same as that found 
in the population (N). Based on this law, it is possible to generalize the results. Law of permanence 
of small numbers. This law states that if two samples are obtained from a population (N), in each 
of the samples (n) there will be a small number of units that have the peculiar characteristics of the 
population, in the same proportion.  
 
In this way, probability samples have many advantages, perhaps the main one being that 
the size of the error in our predictions can be measured. It is even said that the main objective in 
the design of a probability sample is to minimize this error, which is called the standard error 
(Kish, 1995; Kalton and Heeringa, 2003). Probability samples are essential in cross-sectional 
research designs, both descriptive and correlational-causal (opinion polls or surveys, for example), 
where the aim is to estimate variables in the population. These variables are measured and analyzed 

with statistical tests in a sample, where it is assumed that the sample is probabilistic and that all 
elements of the population have the same probability of being selected. The sample units or 
elements will have values very similar to those of the population, so that the measurements in the 
subset will give us accurate estimates of the larger set. The accuracy of these estimates depends 
on the sampling error, which can be calculated.  
 
Sample Size Calculation  
  
The sample size was calculated using EPIDAT version 4.2 for independent samples, 
considering a 95% confidence level (α = 0.05) and 80% statistical power (β = 0.20). A standard 
deviation of 60 seconds was used, taken from previous studies of 5 km races in a similar population 
(Cruz et al., 2022), and a minimum clinically relevant difference of 60 seconds in race time 
between the groups was established. Under these assumptions, an approximate sample size of 8 
participants per group was estimated; however, to strengthen the validity of the study and 
anticipate possible losses during follow-up, it was decided to include 10 participants per group, for 
a total of 20 participants.  
 
Strategy for Recruiting Participants  
  
The recruitment strategy will utilize the researchers' social media accounts (Instagram and 
Facebook) to reach recreational runners who meet the inclusion criteria for this study. Following 
this, an Excel database will be created with the participants' key information to ensure data 
traceability. Communication with the runners will be handled via WhatsApp and email.  
 
Study Development Locations  
  
The study will take place at Saldarriaga Park (Chía), which has a jogging track with a 
certified distance of 520 meters, and the athletic track at San Bartolomé La Merced School, with 
a certified distance of 330 meters in the inner lane and 370 meters in the outer lane. These locations 
were chosen to replicate the real conditions of a road race, as well as for their proximity and 
convenience for the research participants.  
 
Bias Control  
 
The following actions will be taken to control bias:  
 
Selection and Confounding Biases:  
  
To achieve an appropriate intervention in a clinical trial, it is important to use the double-
height technique, where both the researchers and the research participants are unaware of the 
intervention they are about to undergo. (Ledesma, J. & Balaguer, J. 2021), (Sánchez, S. et al. 
2015).  
 
a. Generation of the randomization sequence  
The randomization method used will be block randomization (Ledesma, J. & Balaguer, J. 
2021). The randomization sequence will be generated using a clinical trial randomization 
generator developed by José María Montiel Company of the University of Valencia to 

distribute the 20 athletes into two groups: polarized (POL) and threshold (THR). This task 
will be performed by personnel external to the research group.  
 
b. Concealment  
The concealment of the randomization sequence will be carried out using opaque 
envelopes, sealed and numbered sequentially from 1 to 20. This process will be performed 
by an individual external to the research group.  
 
c. Assignment  
The assignment of participants to each of the groups was in the order in which the initial 
assessments were carried out.  
 
Information Biases  
 
For the pre and post-tests and the monitoring of training, an open trial will be conducted 
where the researchers and coaches will know the groups assigned (POL group and THR group) to 
each of the participants.  
  
Adherence Strategies  
  
To maintain participant adherence to the research, the following activities will be carried 
out:  
• 
Semi-face-to-face and virtual support (via WhatsApp and email) throughout the 
entire research process.  
• 
Define as a training objective, to compete in races such as the “Bimbo Race”, 
“Women's Race” in distances of 5K or such as the “Bogotá Half Marathon” in distances 
of 10K by the middle of the semester of the year 2026.  
• 
Weekly feedback via screenshots of your workouts (summary of your training via 
your GPS systems) to manage all load parameters.  
• 
Individualized and progressive training programs based on the results of the initial 
tests.  
  
Information Gathering  
  
The pre and post-tests will be carried out as shown in Table 2 on different days. On the first 
day, the 5km test will be performed, and 72 hours later the body composition test will be carried 
out, considering the recovery time necessary for the body's systems to return to their normal state 
after maximum effort during the tests. (Chicharro, J. 2008).  
 
 
 
 
 
 
 
 
 

 
 
 
Figure 4. Evaluation schedule  
  
Source: Author's own work.  
Tests 
  
5000 Meter Flat Test (5km)  
  
The 5000-meter (5K) race will be used as a means of analyzing the race time variable. 
Rosa-Guillamón & García-Cantó (2022) state that this race consists of covering 5000 meters, or 
12.5 laps, of official outdoor athletics track at the highest possible speed. This athletic event is the 
shortest distance among those classified as "endurance" or long-distance races, whose main 
characteristic is the predominance of aerobic energy metabolism, expressed through aerobic 
capacity 
and 
power 
as 
a 
function 
of 
time 
and 
intensity 
of 
the 
effort.  
The test-retest reliability of track running tests has demonstrated high consistency coefficients, 
with intraclass correlation coefficient (ICC) values exceeding 0.90 reported when environmental 
variables such as surface, weather conditions, time of day, and warm-up standardization are 
adequately controlled. This level of reproducibility indicates low within-subject variability and a 
reduced standard error of measurement, supporting its use in intervention studies and monitoring 
of aerobic performance. Methodologically, Hopkins (2000) establishes statistical criteria for 
interpreting reliability in sports science, while Atkinson and Nevill (1998) describe procedures for 
quantifying measurement error and ensuring consistency in physical tests applied in a sports 
context—foundations that support the use of the 5000-meter test as a reliable instrument in 
research with adults.  
A review of the literature revealed no studies using the 5-kilometer test with recreational 
runners in Colombia, and therefore no reference values were found. Consequently, the application 
of this test will serve as a reference for other studies related to recreational runners.  
 
Protocol  
  
The 5 km assessment will be conducted in a group setting. Participants must wear 
lightweight, breathable, and comfortable athletic clothing, as well as appropriate footwear, which 
must remain the same for both the initial and final assessments to control for potential equipment-

related biases. Participants will be instructed to maintain their usual breakfast and refrain from 
physical activity for 24 hours prior to the assessment. Additionally, a minimum consumption of 
two liters of water per day will be recommended during the week preceding the assessment to 
ensure optimal hydration.  
 
The 5 km distance will be determined by a 5 km run on the Saldarriaga Park jogging track 
(Chía), with a certified length of 520 meters, and the San Bartolomé La Merced School athletic 
track, with certified lengths of 330 meters in the inner lane and 370 meters in the outer lane. The 
procedure will be carried out according to the guidelines established in the World Athletics 
International Manual (2022), guaranteeing the validity and reliability of the protocol. Running time 
will be recorded independently by two evaluators using Casio HS-3 stopwatches, which have a 
measurement accuracy of up to 1/100 of a second. These stopwatches have been used in research 
such as that of Cossio -Bolaños et al. (2024), where they evaluated the validity and reliability of 
the falls risk scale for older adults. In turn, the dependent variable of body composition will have 
as indicators the percentage of body fat, the percentage of lean mass and the body mass index 
(BMI) and its measurement instrument will be the BIA (bioelectrical impedance) Inbody H20 with 
a degree of error and bias inherent to bioimpedance compared to DEXA, estimated between ±3 
and 5 percentage points (Kim J. et al., 2018; Siedler et al., 2022; Pereira et al., 2019).  
 
Both the initial test and the final test must guarantee the same characteristics mentioned 
above in the protocol, to have the validity and reliability of the respective data analyses.  
 
Body Composition (Inbody bioimpedance scale)  
  
To assess participants' body composition, an Inbody H20 segmental multifrequency 
bioelectrical impedance analyzer, a portable device validated in various populations, will be used. 
This type of device estimates percentage of fat mass, muscle mass index, and total body water 
percentage by measuring the body's electrical resistance (Pereira et al., 2019). Although its use is 
practical and accessible in academic and sports settings, the scientific literature has shown that 
Inbody analyzers exhibit variations compared to the reference method, dual-energy X-ray 
absorptiometry (DEXA).  
 
Comparative studies report that the Inbody 770 shows a high correlation with DEXA in 
estimating fat-free mass and appendicular skeletal mass (ICC = 0.965), although with small 
differences in estimating body fat percentage (Kim HJ et al., 2022). Similarly, Kim J. et al. (2018) 
found prediction errors of approximately ±4.2 percentage points in body fat percentage estimation 
among the Inbody 230, 720, and 770 models compared to DEXA. Likewise, Siedler et al. (2022) 
identified that Inbody devices tend to underestimate body fat percentage by 3.0 to 3.4 percentage 
points, while maintaining high reliability in repeated measurements (ICC ≥ 0.98). Finally, Pereira 
et al. (2019) confirmed almost perfect associations between DEXA and Inbody 270, although they 
noted a systematic bias based on the sex and age of the participants.  
Therefore, it is acknowledged that the absolute values obtained using BIA have an 
approximate margin of error of ±3 to 5 percentage points compared to DEXA. Consequently, this 
study prioritizes the analysis of intraindividual changes (pre- vs. post-intervention) over 
comparison with external normative values.  
 
 

 
 
Measurement Procedure  
  
Participants were asked to arrive having fasted for at least 3 hours, having refrained from 
strenuous exercise in the previous 24 hours, and having emptied their bladder before the 
measurement, following the control guidelines proposed by Kim J. et al. (2018). Assessments were 
always performed at the same time (± 30 minutes) to reduce diurnal variability. Subjects stood 
barefoot on the plantar electrodes and held the hand electrodes according to the manufacturer's 
instructions. Two consecutive measurements were taken, and the average of both was used for 
statistical analysis.  
 
Quality Control and Reliability  
  
To ensure data reliability, variables that can affect BIA, such as hydration, body 
temperature, prior intake, and posture, were controlled. The literature supports that, under 
standardized conditions, Inbody analyzers achieve intraclass correlation coefficients (ICC) greater 
than 0.98 in repeated measurements (Kim J. et al., 2018; Siedler et al., 2022).  
  
Interpretation of Results  
  
Changes in body composition, specifically muscle mass and body fat percentage, will be 
analyzed between the pre- and post-intervention phases. Results were interpreted considering the 
inherent error and bias of bioimpedance compared to DEXA, estimated at ±3 to 5 percentage points 
(Kim J. et al., 2018; Siedler et al., 2022; Pereira et al., 2019). This approach leverages the 
portability and low cost of Inbody without compromising the study's internal validity.  
 
Information gathering  
  
For the collection of information from the tests applied to the variables, an Excel matrix will 
be used and the Inbody app will be used for this task, as evidenced in Table 5 and Table 6 
below.  
  
Table 5. Information collection method  
Variable   
Values or unit of 
measurement   
Verification method  
Collection method  
5K race time   Minutes and seconds (e.g., 
25:45)   
5K Test   
Excel Matrix  
Body 
composition   
% body fat,   
% muscle mass,   
body mass index (BMI 
kg/m²)   
Bioimpedance scale  
Excel Matrix and 
Inbody APP  
Muscle mass   
Kilograms or percentage 
(%)   
Bioimpedance scale  
Excel Matrix and 
Inbody APP  
  
 

 
 
Table 6 Example of the information collection matrix  
Recreational 
corridors  
Race time  
Body composition  
Name  
5K 
starting 
time  
Final 
time  
5K  
initial body 
fat 
percentage  
final body 
fat 
percentage  
% initial 
muscle 
mass  
% final 
muscle 
mass  
initial 
BMI  
final 
BMI  
   
   
   
   
  
  
   
   
   
   
   
   
   
  
  
   
   
   
   
   
   
   
  
  
   
   
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
Ethical Aspects  
 
The principles established by the World Medical Association through the Declaration of 
Helsinki, and Resolution 008430 of the Ministry of Health of Colombia were considered.  
 
Relevance and Social Value of Research  
  
The relevance of this study lies in its contribution to strengthening a physical culture based 
on scientific evidence and understanding the processes that underpin modern training. The analysis 
of exercise intensity models offers tools applicable in both competitive and recreational contexts, 
as well as promoting well-being and health. By understanding how the intensity of effort can be 
modulated to generate efficient and sustainable physiological adaptations, it becomes possible to 
design safer, more personalized training strategies that are consistent with the needs of everyone. 
  
From a social perspective, this research is a valuable resource for athletes, coaches, and 
researchers interested in promoting recreational athletics and conscious, sustainable physical 
activity. The findings allow for a more precise understanding of the parameters that characterize 
the performance of recreational athletes and their relationship to physical fitness and health, 
fostering the development of active lifestyles and overall well-being. Thus, the study transcends 
the sports arena and is projected as a valuable tool for health promotion, disease prevention, and 
human development through responsible physical activity.  
 
Voluntary Participation  
  
Participation in the study was entirely voluntary. The athletes were informed beforehand 
about the nature of the tests, the procedures, and the research objectives. Those who chose to 
participate committed to rigorously adhering to each stage of the experimental protocol, including 
the tests and the training process, to ensure the collection of accurate, valid, and reliable individual 
data.  
 
 
 

Privacy and Confidentiality  
  
The data obtained in this research will be processed in accordance with the principles of 
privacy and informed consent of the participants. Participants will be duly informed about the 
public use of their information, always ensuring confidentiality. Only researchers will have access 
to the files, which are stored on systems with restricted access. Furthermore, personal identification 
data (such as national identification number, address, and telephone number) will be deleted, and 
each participant will be assigned a code. No information that could compromise their privacy will 
be disclosed, and the data will only be used until the publication of the study results.  
 
Risk Statement  
  
This research was classified, according to Article 11 of Resolution 008430, as a minimal-
risk study, in which maximal exercise was applied to healthy volunteers. The participants in the 
interventions had experience in physical training programs and therefore had a level of adaptation 
to the planned activities. Furthermore, they were in good health, which allowed their participation 
in this research to be considered safe.  
Through training, the athlete will be presented with the informed consent form, which 
explicitly details the study, their duties, their rights, the possible risks, the benefits, among other 
aspects that will allow them to make a conscious and pressure-free decision to participate or not 
in the study. The informed consent form will be signed during that same training and/or the day 
before the first initial assessment that the athlete underwent, which will be a requirement to 
participate in the research.  
 
Strategies to Minimize Risks  
  
Strategies to minimize risks are established based on the results obtained in initial tests and 
standardized indirect tests (to obtain maximum heart rate), supported by scientific evidence and 
literature. Workloads will be individually designed and monitored through heart rate tracking, 
following a structured plan grounded in scientific evidence and respecting training principles and 
the specific population. Assessments and interventions will be conducted in safe environments, 
equipped with first aid supplies, and will be carried out by qualified personnel experienced in 
applying the protocols.  
 
Personal Benefits  
  
Upon completion of the research, participants are expected to strengthen their 
understanding of the physiological, psychological, and methodological variables that influence 
performance and health. Using an evidence-based approach, the aim is to promote safer, more 
mindful, and self-care-oriented training practices. Furthermore, the goal is for athletes to 
understand the importance of proper training load planning and management, along with regular 
physical assessments, to optimize performance and prevent injuries. This educational process 
extends beyond the sporting arena, fostering the development of sustainable habits that integrate 
body, mind, and well-being, promoting responsible, balanced, and fully conscious training 
practices.  
 
 

Institutional Benefits  
  
The results of this research will provide significant value to the institutions involved, both 
in strengthening their academic projection and in the practical application of their training and 
sports programs.  
For the National Pedagogical University, the study represents an opportunity to expand its 
participation in emerging research areas related to new training trends applied to recreational 
runners, a field still relatively unexplored both nationally and internationally. This will allow the 
institution to position itself as an academic leader in applied research on performance and health, 
contributing to the development of contextualized scientific knowledge with social impact.  
Similarly, the results can be integrated into university training, professional development, 
and outreach programs, strengthening the connection between teaching, research, and professional 
practice. In this way, the University will benefit not only in terms of scientific output but also in 
the development of strategies that promote safe, responsible, and evidence-based sports practices.  
For their part, associated sports institutions such as clubs or recreational running teams will 
have access to validated tools and methodologies that will allow them to optimize the planning, 
control and evaluation of their training processes, improving the performance and well-being of 
their athletes.  
 
Intervention  
  
These are two intervention groups that will use the polarized and threshold TID models as 
shown below:  
1. TID POL  
2. TID THR  
 
TID POL and THR models  
  
Regarding the intervention, it will last 12 weeks, a period during which physiological 
adaptations to resistance training occur, as Neuman (1994) states with chronological cycles of 8 to 
12 weeks. Now, we will go into detail about how the interventions will work with the DIE models. 
For the TID POL model, the intensity distribution was as follows: Zone 1 (80%), Zone 2 (5%), 
and Zone 3 (15%). For the TID THR model, the intensity distribution was as follows: Zone 1 
(50%), Zone 2 (40%), and Zone 3 (10%), in the three-zone model according to Skinner and 
McLellan (1980). This will be monitored using the theoretical heart rate model developed by 
Tanaka, Monahan, and Seals (2001). The training periodization for the two TID models was 
designed using a 3:1 weekly block structure, meaning three weeks of training followed by one 
week of recovery. The weekly training frequency will consist of four running sessions, each 
including 10 minutes of running technique drills and two strength training sessions. The running 
sessions will have the same training volume across the three blocks, which span weeks 1-3, 5-7, 
and 9-11. The first week of each block will have a volume of 240 minutes, the second week 300 
minutes, and the third week 360 minutes. Recovery weeks 4, 8, and 12 will have a volume of 180 
minutes. Strength training sessions will be held on Mondays and Fridays, with a weekly volume 
of 60 minutes. Rest days from the running component will be Monday, Friday, and Sunday. The 
general structure of the intervention is shown in Table 7 below.  
 
 

Table 7. General structure of interventions with TID models  
  
Training 
component  
Load component  
Weeks of intervention  
1  
2  
3  
4  
5  
6  
7  
8  
9  
10  
11  
12  
Career  
Volume  
(Minutes/  
Week)  
240  
300  360  
180  
240  
300  
360  
180  
240  
300  360  
180  
Training frequency  
(Sessions/  
Week)  
4  
4  
  
4  
4  
  
4  
  
4  
4  
4  
4  
4  
4  
4  
Force  
Volume  
(Minutes/  
Week)  
60  
  
60  
  
60  
  
60  
  
60  
  
60  
  
60  
  
60  
  
60  
  
60  
  
60  
  
60  
  
Training frequency  
(Sessions/  
Week)  
2  
  
2  
  
2  
  
2  
2  
2  
2  
2  
2  
2  
2  
2  
  
Running technique  
  
Volume  
(Minutes/  
Week)  
40  
40  
40  
40  
40  
40  
40  
40  
40  
40  
40  
40  
Training frequency  
(Sessions/  
Week)  
4  
4  
4  
4  
4  
4  
4  
4  
4  
4  
4  
4  
Source: own elaboration  
  
Load Quantification  
  
To quantify the training load, the Training Impulse Method (TRIMP) developed by Muñoz 
Pérez (2016) will be used. This method assigns a score to each minute the athlete spends in each 
training zone of the three-phase model according to Skinner and McLellan (1980). The scoring is 
as follows: one point for each minute in zone 1, two points for each minute in zone 2, and three 
points for each minute spent in zone 3. Therefore, if the runner spent 35 minutes in zone 1, 10 
minutes in zone 2, and 3 minutes in zone 3, their TRIMP score would be 64. The use of this 
quantification method aims to ensure that all runners in the same group receive similar training 
loads.  
  
Specific Training Programs for Intervention Groups  
  
Table 8and Table 10 below describe the training program for each group to be involved:  
Table 8. POL group training program  
 
Cluster  
Day  
Week  
(1,5,9)  
Week  
(2,6,10)  
Week  
(3,7,11)  
Week  
(4,8,12)  
POL  
Monday  
Rest  
Rest  
Rest  
Rest  
Tuesday  
Z1 C15 min  
Z1-29 min  
Z1Vc-10 min  
Z1 C 15 min  
Z1-45 min  
Z1Vc-10 min  
Z1 C15 min  
Z1-61 min  
Z1Vc-10 min  
Z1 C15 min  
Z1-13 min  
Z1Vc-10 min  
Wednesday  
Z1 C 15 min  
Z1-29 min  
Z2-6 min  
Z1Vc-10 min  
Z1 C15 min  
Z1-45 min  
Z2-9 min  
Z1Vc-10 min  
Z1 C15 min  
Z1-61min  
Z2-9 min  
Z1Vc-10 min  
Z1 C 15 min  
Z1-13 min  
Z2- 4.5 min  
Z1Vc-10 min  

Thursday  
Z1 C15 min  
Z1-29 min  
Z1Vc-10 min  
Z1 C15 min  
Z1-45 min  
Z1Vc-10 min  
Z1 C15 min  
Z1-61 min  
Z1Vc-10 min  
Z1 C15 min  
Z1-13 min  
Z1Vc-10 min  
Friday  
Rest  
Rest  
Rest  
Rest  
Saturday  
Z1 C15 min  
Z3 (6x6 min)  
36 min  
Z2 R (6x1)  
6 min  
Z1 Vc-15 min  
Z1 C15 min  
Z3 (6x7.5min) 45 
min  
Z2 R (6x1)  
9 min  
Z1 Vc-15 min  
Z1 C15 min  
Z3 (9x6 min) 54 
min  
Z2 R (9x1)  
9 min  
Z1 Vc-15 min  
Z1 C15 min  
Z3 (9x3 min)  
27 min  
Z2 R (9x0.5)  
4.5 min  
Z1 Vc-15 min  
Sunday  
Rest  
Rest  
Rest  
Rest  
Z1 (Min)  
192  
240  
288  
144  
Z2 (Min)  
12  
15  
18  
9  
Z3 (Min)  
36  
45  
54  
27  
Volume 
(min)  
240  
300  
360  
180  
Training 
load  
TRIMPS  
  
324  
  
405  
  
  
486  
  
243  
Abbreviations: Z1 (Zone 1), Z2 (Zone 2), Z3 (Zone 3), C (Warm-up), Vc (Cool-down), R (Rest)  
  
Table 9. Example of a POL training session  
 
Training session (Wednesday, week 1)  
Heating  
Zone 1  
15 minutes  
Central phase  
Zone 1  
29 minutes  
Central phase  
Zone 2  
6 minutes  
Back to calm  
Zone 1  
10 minutes  
  
Table 10. THR Group Training Program  
Cluster  
Day  
Week  
(1,5,9)  
Week  
(2,6,10)  
Week  
(3,7,11)  
Week  
(4,8,12)  
  
  
  
  
THR  
Monday  
Rest  
Rest  
Rest  
Rest  
Tuesday  
Z1 C 15 min  
Z1 7 min  
Z2 30 min  
Z1 Vc 10 min  
Z1 C 15 min  
Z1 17 min  
Z2 38 min  
Z1 Vc 10 min  
Z1 C 15 min  
Z1 27 min  
Z2 46 min  
Z1 Vc 10 min  
Z1 C 10.5 min Z1 5 
min  
Z2 22 min  
Z1 Vc 9 min  
Wednesday  
Z1 C 15 min  
Z1 7 min  
Z2 30 min  
Z1 Vc 10 min  
Z1 C 15 min  
Z1 17 min  
Z2 38 min  
Z1 Vc 10 min  
Z1 C 15 min  
Z1 27 min  
Z2 46 min  
Z1 Vc 10 min  
Z1 C 10.5 min Z1 5 
min  
Z2 22 min  
Z1 Vc 9 min  
Thursday  
Z1 C 15 min  
Z1 7 min  
Z2 30 min  
Z1 Vc 10 min  
Z1 C 15 min  
Z1 17 min  
Z2 38 min  
Z1 Vc 10 min  
Z1 C 15 min  
Z1 27 min  
Z2 46 min  
Z1 Vc 10 min  
Z1 C 10.5 min Z1 5 
min  
Z2 22 min  
Z1 Vc 9 min  
Friday  
Rest  
Rest  
Rest  
Rest  
Saturday  
Z1 C 14 min  
Z1 C 14 min  
Z1 C 14 min  
Z1 C 7.5 min  

Z3(6x4)  
24 min  
Z2 (6x1)  
6 min  
Z1 Vc 10 min  
Z3(6x5)  
30 min  
Z2 (6x1)  
6 min  
Z1 Vc 10 min  
Z3(6x6)  
36 min  
Z2 (6x1)  
6 min  
Z1 Vc 10 min  
Z3(6x3)  
18 min  
Z2 (6x1)  
6 min  
Z1 Vc 6 min  
Sunday  
Rest  
Rest  
Rest  
Rest  
Z1 (Min)  
120  
150  
180  
90  
Z2 (Min)  
96  
120  
144  
72  
Z3 (Min)  
24  
30  
36  
18  
Volume 
(Min)  
240  
300  
360  
180  
TRIMPS 
training load  
  
384  
  
480  
  
576  
  
288  
Abbreviations: Z1 (Zone 1), Z2 (Zone 2), Z3 (Zone 3), C (Warm-up), Vc (Cool-down), R (Rest). Source: 
Author's own elaboration.  
 
Table 11. Example of a THR training session  
Training session (Thursday, week 1)  
Heating  
Zone 1  
15 minutes  
Central phase  
Zone 1  
7 minutes  
Central phase  
Zone 2  
30 minutes  
Back to calm  
Zone 1  
10 minutes  
  
  
Programming and Monitoring  
  
The training program and monitoring are managed by the project's researchers. Runners 
will use GPS devices (Garmin, Polar, Apple) that will generate summaries including duration, 
intensity, pace, heart rate, and time spent in each training zone. This data will be collected weekly 
through screenshots of these summaries, focusing on the time participants spent in each training 
zone.  
 
Schedule  
  
Table 12. General schedule  
Activity  
Jan  
Feb  
Sea  
April  May  Jun  
Jul  
August  
Sep  
Oct  
Nov  
Call for 
applications  
  
x  
  
  
  
  
  
  
  
  
  
Initial 
assessments  
  
  
x  
  
  
  
  
  
  
  
  
Final 
assessments  
  
  
  
  
x  
  
  
  
  
  
  
Data analysis  
  
  
  
  
  
x  
  
  
  
  
  
Literature 
review  
x  
x  
x  
x  
x  
x  
x  
x  
  
  
  
Article writing  
  
  
  
  
  
  
x  
  
  
  
  

Socialization of 
results  
  
  
  
  
  
  
  
x  
  
  
  
Source: own elaboration.