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		<title>Bodybuilding and weightlifting exercises</title>
		<link>https://fitenium.com/en/bodybuilding-and-weightlifting-exercises/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=bodybuilding-and-weightlifting-exercises</link>
		
		<dc:creator><![CDATA[Borja Zurita]]></dc:creator>
		<pubDate>Sat, 29 Jul 2023 10:34:06 +0000</pubDate>
				<category><![CDATA[Training]]></category>
		<category><![CDATA[bodybuilding]]></category>
		<category><![CDATA[chinesemethod]]></category>
		<category><![CDATA[volume]]></category>
		<category><![CDATA[weightlifting]]></category>
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					<description><![CDATA[Bodybuilding and weightlifting exercises As we have commented previously in this blog, the first thing you have to define when you train are your goals. Do I want to develop my upper body? Do I want to increase my strength?...]]></description>
										<content:encoded><![CDATA[<h2>Bodybuilding and weightlifting exercises</h2>
<p>As we have commented previously in this blog, the first thing you have to define when you train are <strong>your goals.</strong> Do I want to develop my upper body? Do I want to increase my strength? I want to lose weight? Etc.</p>
<p>The difference between athletes who practice bodybuilding exercises and weightlifting is visually noticeable since their objectives are different. Anyway, there are many people who doubt about the training that they follow each other to achieve results. In this post we will review the types of training and objectives of each discipline to understand it better.</p>
<p>After an introduction to these disciplines, we also leave you with several <strong>bodybuilding exercises</strong> in case you want to focus on gaining volume.</p>
<h2><strong>Bodybuilding: the focus is on size.</strong></h2>
<p>Bodybuilders often lift weights not with the goal of building strength but with the goal of increasing the size of their muscles. Bodybuilders will obviously build strength as they train, but this will be an <strong>added effect</strong> to their main goal of bulking up.</p>
<p>Bodybuilders who lift weights to increase their size usually work to get the blood to carry as much oxygen and nutrients to the muscles. This gives them a nice feeling and keeps them active while training at the limit. The type of lifting that bodybuilders do has the goal of creating micro-tears in the muscle, forcing the body to repair it and thus increasing storage capacity. The body repairs, causes muscle growth. This training procedure is known as <strong>hypertrophy</strong>.</p>
<p>As muscles grow they can develop more power, which means they can exert greater force during workouts. However, the energy stored in bulky muscles is not the same as actual strength, and for this reason bodybuilders tend to be weaker than they appear.</p>
<p>The term bodybuilding generally refers to competitive displays, which is why many bodybuilders focus on working their bodies to look their best on stage.</p>
<p>Read until the end to find 5 bodybuilding exercise programs</p>
<h2><strong>Strength training: the focus is on muscle power.</strong></h2>
<p>When it comes to strength training, there is one main goal with training: <strong>to increase the amount of power</strong> your muscles can develop to lift loads. The size and shape of the muscles do not matter as long as they can provide maximum force during the lift.</p>
<p>Strength training focuses on exercises with low repetitions and heavy weights, and is focused on teaching your central nervous system how to use your motor units when you lift. Lifting is focused on increasing the strength of your muscles, strengthening your joints, strengthening your bones, and developing stronger tissues.</p>
<p>Unlike bodybuilding exercises, in strength exercises the strength of athletes does not have to be reflected in a <a href="https://fitenium.com/es/como-tener-fisico-perfecto/">perfectly sculpted body.</a> Strength athletes typically have a higher percentage of body fat, and their body shapes tend to be more solid and blocky rather than sleek and lean as is the case for bodybuilders. In any case, when it comes time to use the muscles, <strong>strength athletes can develop more strength</strong> and last longer than bodybuilders.</p>
<h2><strong><a href="https://fitenium.page.link/web%20"><img class="aligncenter wp-image-25030 size-medium" src="https://fitenium.com/wp-content/uploads/2019/02/Fitenium-Logo-1-300x165.png" alt="" width="300" height="165"></a></strong></h2>
<h2><strong>Weightlifting </strong></h2>
<p>Weightlifting is a particular type of strength training to increase muscular strength and the size of skeletal muscles. In weightlifting, bars, dumbbells and weight discs are used to train concentric and exectric muscle contraction exercises. This discipline also uses a series of specialized equipment to train specific muscle groups.</p>
<p>Weightlifting is also an <strong>Olympic discipline</strong> in which athletes attempt to lift their maximum weight in a one repetition lift of a barbell loaded with weight plates. The two weightlifting competitions are the snatch and the two-stroke.</p>
<p>The snatch has a wide grip and a single lift while in the two-stroke the bar is raised in two different sequences. In Olympic weightlifting, each athlete receives three attempts for each of the categories: snatch and two times. The sum of the total weight of the two highest lifts will determine the weight lifted to be taken into account in a competition. An athlete who fails to perform a valid snatch and two-stroke lift will receive an incomplete grade for the competition.</p>
<p>Although there are not many globally competitive Olympic lifters, the lifts developed in the sport of weightlifting, and in particular the components of the lifts such as squats, deadlifts, etc. They are frequently used by elite athletes specialized in other disciplines and sports to train explosive and functional strength.</p>
<p>You may have also heard of <strong>Chinese Weightlifting.</strong> Chinese Weightlifting is the training system for weightlifters from the communist country with which they are achieving great results.</p>
<h2><strong>5 bodybuilding exercises to increase volume</strong></h2>
<p>If you are interested in gaining muscle mass, one of your highest priorities is to determine which method will work for you. Here we present 5 training programs to increase your volume.</p>
<h3><strong>#1. The 5X5 program</strong></h3>
<p>The 5X5 program is quite popular among those who are interested in gaining the maximum amount of <strong>strength and muscle mass.</strong> The objective of this program is to perform three exercises focused on the main muscle groups of the body: the upper body and the lower body, performing series of 5 repetitions for each exercise. At the end of each training you can add series of isolated bodybuilding exercises if you need them, although they are not required by the program itself.</p>
<h3><strong>#2. german volume training</strong></h3>
<p>The next program to build bulk is the German Bulk Training program. This training is similar to the 5X5 program since it will also ask you to do a good number of exercises, but it differs in that the number of repetitions increases up to <strong>10 repetitions and 10 series for each exercise.</strong></p>
<p>The goal of this program is to focus on two muscle groups a day, alternating between them over the course of three training days a week.</p>
<h3><strong>#3. The FST-7 training program.</strong></h3>
<p>The third type of volume training program that is currently in vogue is FST-7 training. This program is not specifically based on a series of exercises that you have to perform or a protocol to divide the muscle groups of your body. This program gives you the guidelines for what you should be training in the last bodybuilding exercise for each body part you worked in that session.</p>
<p>The acronym FST comes from the English Fascial Stretch Training, which indicates that one of the main objectives that this program tries to achieve <strong>is to stretch the soft connective tissue that is found around your muscles</strong> as well as in the rest of your body.</p>
<p>This fabric is primarily responsible for helping to maintain the structural integrity of your body, providing support and protection, and works as a shock absorber when you perform your workouts both in and out of the gym.</p>
<p>When the soft tissue is stretched, you will see increases in muscle growth. There will also be a greater contribution of minerals, amino acids and oxygen to the tissues.</p>
<p>With this workout, perform seven sets of 15 repetitions of the last exercise you did for each muscle group. It is important that you make the rest periods between these sets shorter and shorter &#8211; about 30 seconds total.</p>
<p>Keep in mind that you will probably have to use a lower weight than you normally used for each bodybuilding and weightlifting exercise since you are now doing a much higher total number of repetitions.</p>
<h3><strong>#4 Split upper and lower body training.</strong></h3>
<p>The fourth type of exercise is the upper and lower body. This routine is based on training exchanging muscle groups so that you train each group twice a week.</p>
<p>This system is a <strong>good option for beginners and lifters</strong> who are looking to gain mass since it allows you to get plenty of rest throughout the week. If you are an advanced athlete you can also increase the number of series and define your selection of exercises and rests to increase your muscle gains at any level.</p>
<p>On the other hand, this system forces you to do four sessions a week. If you have trips or other commitments, following the training calendar can be a problem for you since it is not very flexible.</p>
<h3><strong>#5. full body workout</strong></h3>
<p>Finally we stop at <strong>full body training.</strong> The 5 x 5 program could also be considered a full body program since you work virtually all major muscle groups with the three exercises of your choice. In any case, true full body training programs will give you a specific exercise for each muscle group: quads, hamstrings, chest, back and shoulders (arms are worked when you work chest and back).</p>
<p>In addition to these lifts and bodybuilding exercises dedicated to muscle groups, you can also work some isolated exercises to train individual muscles.</p>
<p>In conclusion, remember that workouts aimed at bodybuilders are focused on gaining volume, while exercises used by strength athletes seek to gain muscle power. Weightlifting is a discipline within strength exercises and has two Olympic categories that are the snatch and the two-stroke.</p>
<p>If you want to start doing bulking training, you can follow our recommendations on the training programs most used by athletes. Always remember to ask specialized professionals who know your physical situation when defining your training to obtain the best results in a timely manner.</p>
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		<title>Intensity and volume in training loads</title>
		<link>https://fitenium.com/en/intensity-and-volume-in-training-loads/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=intensity-and-volume-in-training-loads</link>
		
		<dc:creator><![CDATA[Adrian Garcia]]></dc:creator>
		<pubDate>Fri, 28 Jul 2023 12:45:31 +0000</pubDate>
				<category><![CDATA[Character of the effort]]></category>
		<category><![CDATA[Fatigue]]></category>
		<category><![CDATA[strength training]]></category>
		<category><![CDATA[training schedule]]></category>
		<category><![CDATA[excessive loads]]></category>
		<category><![CDATA[intensity]]></category>
		<category><![CDATA[volume]]></category>
		<guid isPermaLink="false">https://fitenium.com/intensity-and-volume-in-training-loads/</guid>

					<description><![CDATA[Intensity and volume in training loads The organization of intensity and volume in training loads is a basic aspect in training programming. In this entry the considerations of combining volume and intensity are evaluated and the key questions regarding the...]]></description>
										<content:encoded><![CDATA[<h2>Intensity and volume in training loads</h2>
<p>The organization of intensity and volume in training loads is a basic aspect in training programming. In this entry the considerations of combining volume and intensity are evaluated and the key questions regarding the programming of the loads are answered.</p>
<p><em>In this series of articles we deal with some of the most important concepts of strength training, collecting notes from the recently published book <a href="https://www.libreriadeportiva.com/libro/fuerza-velocidad-y-rendimiento-fisico-y-deportivo_73450"><strong>Strength, Speed ​​and Physical and Sports Performance</strong></a> written by renowned researchers <strong>Juan José González Badillo and Juan Ribas Serna.</strong></em></p>
<h4>summary</h4>
<ul>
<li><em>By increasing or keeping the intensity and/or volume stable, the effect on training is positive. In all other cases the effect is not defined or is negative.</em></li>
<li><em>Stimulus levels should be applied when it best suits the subject&#8217;s capacity and produces a positive effect.</em></li>
<li><em>The application of excessive loads almost always has negative consequences such as the risk of injury and the difficulty of executing a correct technique.</em></li>
</ul>
<p>&nbsp;</p>
<p>Changes in the training load are produced by modifying some of its factors: volume, intensity and type of exercise. Regarding the exercises, the difficulty and load increase, regardless of other factors, as a greater number of joints and muscle groups are involved, which is generally accompanied by greater technical difficulty and greater mechanical work due to unit of action (repetition).</p>
<p>But if we keep an exercise or group of exercises stable, the changes in volume and intensity are what will determine if the changes in the load are positive, negative or null for performance. When we talk about intensity, unless otherwise stated, we always refer to relative intensity, not absolute (weight).</p>
<h4><img class="aligncenter wp-image-29482 size-full" src="https://fitenium.com/wp-content/uploads/2020/09/WiKEnlt6Z3U.jpg" alt="Intensity and volume in training loads" width="640" height="427"></h4>
<h4>Changes in the training load are produced by modifying some of its factors: volume, intensity and type of exercise.</h4>
<p style="text-align: left;">Taking into account all the possible combinations in the change of these factors within a training cycle: increase or decrease <a href="https://fitenium.com/componentes-de-la-carga-en-el-entrenamiento-el-volumen/">the volume</a> y <a href="https://fitenium.com/componentes-de-la-carga-en-el-entrenamiento-la-intensidad/">The intensity </a>As well as the possibility that one or both of them remain stable, there can be nine situations, which we are going to analyze below, indicating their effect on performance depending on the way and when they are used.</p>
<h5><strong>1. The volume and intensity increase</strong>: the effect will tend to be positive.</h5>
<p>Whenever this combination occurs within the training process, there is an initial improvement in performance, unless both variables (volume and intensity) are already at a very high degree of load in relation to the subject&#8217;s possibilities. If this last circumstance occurs, the effect will be null in the best of cases, and almost always negative. If this circumstance does not occur, and therefore the effect is positive, it must be considered that this load trend would only be valid for three or four weeks in a row, and must be modified later.</p>
<p>Only very slight increases in the load and with very low training frequencies allow this trend to be maintained for a longer number of weeks.</p>
<h4>this trend of the loads would only be valid for three or four weeks in a row, and must be modified later</h4>
<h5><strong>2. The volume increases and the intensity remains stable</strong>: the effect will tend to be positive</h5>
<p>The effect will be positive if the trend does not continue. Only between two and six sessions would the positive effects be maintained without increasing the intensity. It is a form of progression suitable for the Beginnings of a training cycle.</p>
<h5><strong>3. The volume increases and the intensity decreases</strong>: the effect is not defined.</h5>
<p>It would be useful when you want to increase muscle mass or you want to make a deep change in the training system to break a state of negative adaptation (stagnation). However, in any of these cases, it would always be necessary to increase the intensity again after a few workouts, otherwise the aforementioned objectives would not even be obtained.</p>
<h5><strong>4. The volume remains stable and the intensity increases:</strong> the effect will tend to be positive.</h5>
<p>It is an always positive trend change for strength performance. Its best application may be at the point in the cycle when a considerable volume of work has already been achieved. One or two weeks with this tendency can have a very good effect.</p>
<p><strong><img class="aligncenter wp-image-29474 size-full" src="https://fitenium.com/wp-content/uploads/2020/09/REafAGAia54.jpg" alt="intensity and volume" width="640" height="960"></strong></p>
<h5><strong>5. Volume and Intensity remain stable</strong>: the effect is not defined.</h5>
<p>This situation should not continue for more than two or three sessions in a row. If this is done, the effect could be positive, otherwise it would be negative.</p>
<h5><strong>6. The volume remains stable and the intensity decreases:</strong> the effect will tend to be negative</h5>
<p>We cannot expect anything positive from this trend that is worthwhile in strength performance: there is no increase in the stimulus in any of the variations, and neither could we expect an improvement in form due to the recovery effect, since the volume does not decreases. Seeking recovery by only reducing intensity is not appropriate for strength.</p>
<h4>Seeking recovery by only reducing intensity is not appropriate for strength.</h4>
<h5><strong>7. The volume decreases and the intensity increases:</strong> the effect will tend to be positive.</h5>
<p>This trend may be valid for: a) maintain the performance achieved b) recover the body without loss of strength and c) occasionally, to improve performance after a high volume phase.</p>
<p>Its most effective application occurs in the 2nd phase of the training cycle.</p>
<h5><strong>8. The volume decreases and the intensity remains stable:</strong> the effect will tend to be positive</h5>
<p>It is positive only as a recovery, well in a week of unloading before a competition.</p>
<h5><strong>9. Volume and intensity decrease:</strong> the effect is not defined.</h5>
<p>It would never offer positive effect for strength improvement. It would make sense as a form of deep recovery in phases of active rest. Within the training cycle it can be used in a session as a way of unloading.</p>
<p>To all these possible combinations, we should add the combination that we could consider the most favorable and desired, which is the one in which the relative intensity remains practically stable while the absolute intensity increases, with volumes also practically stable or minimal oscillations, or In any case, a downward trend. This trend will continue as long as it remains positive, during all training cycles.</p>
<p>As a synthesis of the previous assumptions about adaptation to strength training, we can say that each level or degree of stimulus should be applied at the time it is most necessary, best suited to the athlete&#8217;s capacity, and produce a sufficient positive effect. Once a charge has been used successfully, it has little or no effect if we want to use it again.</p>
<h4>Each level or degree of stimulus should be applied at the time it is most necessary, best suited to the athlete&#8217;s ability, and produce a sufficient positive effect.</h4>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p>Therefore, if we are capable of providing successive adjusted stimuli, each time more demanding, within the strength needs, and with real variability, it is more likely that the progression in the results will be maintained for longer and greater. By using a small stimulus, but one that is enough to provide great progress, we are not only applying adequate training, but we are preparing the athlete to be able to face other higher loads when necessary.</p>
<p>If heavy loads are used, even if they are not necessary, there is also great initial progress, but this almost always has negative consequences: risk of injury, rendering useless the application of lighter loads that would have been effective at the time, not creating the adequate conditions for the correct learning of the technique in some exercises that require it, reduce and range of stimuli applicable throughout sporting life, and therefore, the possibilities of variability.</p>
<p>As a conclusion, the following can be stated:</p>
<ol>
<li>In strength training it is easy to progress in the first cycles of work, but this should not lead to violently increasing the training demands with the intention of progressing more quickly. The efforts required by each stage of sporting life must be respected. A lower degree of effort does not mean that progress is necessarily less. An effort adjusted to the real needs of the athlete can mean a greater and better development of strength both in the short and long term.</li>
<li>The degree of effort must be strictly adjusted to the circumstances of age and experience.</li>
<li>Almost any training can be effective for a few weeks or months, but progression over many years, improvement in technique, and joint and muscle health are more likely to be achieved with rational training, according to the adaptation assumptions indicated.</li>
<li>The magnitude of the load depends fundamentally on the volume, the intensity and the exercise that is used.</li>
<li>The introduction of a higher load magnitude is allowed and justified when the preceding ones have been assimilated. That is, when these loads are below the current stimulation threshold of the neuromuscular system, and therefore the organism no longer presents a positive reaction to said loads. In this situation we can say that the charges used up to now have already produced their effect.</li>
<li>The charges lose their effect firstly in absolute terms, due to the continued use of the same weight, and then in relative terms, due to the continued use of the same percentage. This means that as performance increases, smaller percentages may become less effective.</li>
<li>Certain modifications of the volume and intensity of the training produce a positive effect, while others have no or negative effect.</li>
<li>The positive effect of a cargo modification and its validity period also depend on the circumstances in which said modifications occur.</li>
</ol>
<h4>The application of large loads almost always has negative consequences: risk of injury, making useless the application of more effective light loads, difficulty in learning the correct technique, etc.</h4>
<p>But before programming a training session, a series of questions must be answered, the answers of which will establish the reality on which action must be taken. Once this reality is known, it will be necessary to also take into account a series of basic methodological considerations derived from the theory of training and from experience in sports practice. These considerations can be addressed through a series of key questions about training scheduling.</p>
<h5><strong>When should you start strength training?</strong></h5>
<p>The moment of the start of strength training in an athlete who is going to the competition could be determined in the first place by the needs or strength demands of the sport or sports specialty. However, it is considered that starting to improve strength through training especially aimed at this objective from the very beginning would always be positive whatever strength needs may be in the future.</p>
<p>The important thing, and the &#8220;risk&#8221;, is not the moment to start the strength training, but the way to carry out said training. The correct training of strength from the earliest ages does not present any indication in the physical and technical development of the athlete and it is recommended as a way to avoid injuries and improve performance (Payne et al., 1997).</p>
<p><strong><img class="aligncenter size-full wp-image-28986" src="https://fitenium.com/wp-content/uploads/2020/09/lyKYUOdvsEs.jpg" alt="" width="640" height="852"></strong></p>
<h5><strong>How much force do you have to develop?</strong></h5>
<p>In this case, when we ask ourselves this question, we are referring to the maximum degree of force development, assessed through the RM estimation, but not by its direct measurement. The degree of development of these strength values ​​must be directly related to the needs of the sport or specialty. To know our objectives, the strength values ​​achieved by the most outstanding athletes in the specialty can be taken as a reference point, but mainly the effect of strength improvement on performance improvement in competition or in specific tests can be considered.</p>
<p>But in addition to the maximum force expressed as , the useful force (another maximum force value) must also be considered, in other words, the maximum force value that the athlete is capable of applying when performing the specific gesture, as well as the ability to produce force in the unit that the improvement of the maximum force (in this case the estimation of the RM) presents a positive relationship with the improvement of the performance and with the useful force, the development of the force must continue to be maintained .</p>
<p>If there is an increase in strength but it is not accompanied by an improvement in performance, we should consider reducing resistance training and looking only to maintain it until the specific performance improves. There may come a time when inadequate strength training (even if RM improvement occurs) is related to the loss of one&#8217;s own specific performance. In this case, strength training would have to be reduced or changed—or both.</p>
<h4><strong>What exercises should be used?</strong></h4>
<p>Although in the first steps of training an athlete it is necessary to stimulate all muscle groups in a balanced way and ensure a solid strengthening of tendons and joint ligaments, specific performance is achieved by training those movements, muscle groups and responsible energy systems. of performance in competition.</p>
<p>For this reason, since the athlete begins the path of high performance (since he decides to practice a sport with the aspirations of becoming a high-level athlete in the future), the work program must especially include only non-specific exercises. most useful and specific strength-building exercises applicable to your specialty.</p>
<h4>the work program should especially include only the most useful non-specific exercises and the most specific exercises for strength development applicable to your specialty</h4>
<h5><strong>How often do you have to train?</strong></h5>
<p>As infrequently as it produces sufficient force development. In some moments the frequency should be only what is necessary to maintain the force. The training frequency must necessarily increase as sporting life progresses, although the margin of increase is very small if the same volume is not distributed in different sessions. The greater need for strength in a specialty also demands a greater frequency of training.</p>
<p>In some cases, the limiting factor of the training frequency is not the lesser need for strength in the specialty, but the frequency of competitions and the possible interference between training and the development of more or less antagonistic qualities. A higher frequency does not necessarily mean a higher load. The same proposed load (understood as a synthesis of volume and intensity) carried out in two sessions, on the same day or on separate days, implies less real load than if it is done in a single session. That is, we would talk about more frequency but less <a href="https://fitenium.com/la-fatiga-en-distintos-tipos-de-esfuerzos/">fatigue</a>.</p>
<p><strong><img class="aligncenter size-full wp-image-28626" src="https://fitenium.com/wp-content/uploads/2020/09/Apejl7P4-vk.jpg" alt="" width="640" height="960"></strong></p>
<h5><strong>What relative intensity (%1RM or speed of the first repetition) should be used?</strong></h5>
<p>The most suitable maximum relative intensity of training is directly related to the strength needs in the specialty. That is, the greater the need for strength, the greater the maximum intensity that must be reached through sports life, as well as the frequency with which it is used. But it is convenient to add some other orientations that complete this aspect that is so decisive and dangerous in the training schedule. Among these aspects, we highlight the following <span style="text-decoration: underline;"><strong>factors to define the relative intensity:</strong></span></p>
<p><em>The subject&#8217;s initial training level.</em> The degree of training of the subject takes precedence over the strength needs of the sport. It is not possible to train with the typical intensities used in a specialty if the athlete, given his level of training, neither can nor needs to use high intensities to sufficiently improve his strength.</p>
<p> <em>Speed ​​and phase-angle-position of the competition gesture in which the force will be applied.</em> The speed at which the force will be applied in competition is decisive in the choice of training intensity. It will be necessary to consider to what extent the improvement in maximum strength (1 RM) has an effect on the force applied at competition speed (useful force). The force applied at the competition speed will be the reference point to assess the effects of strength training. Many of the exercises and training intensities will need to be close to competition speed and the angle at which force is applied.</p>
<p><em>Time that can and should be devoted to strength training.</em> The strength training load is subordinated to the frequency of competitions. When competitions are very frequent throughout the season it is necessary to allow recovery before and after each test, which means that the time dedicated to strength training cannot be high. The time that can be dedicated depends on this circumstance. Although, on the other hand, it would be necessary to consider the time that must be dedicated to strength training for the desired effects to be produced. It is necessary to take into account both conditions and adjust the training so that it is effective and not useless.</p>
<p><em>What is the specific musculature involved and the type of muscle activation? </em>Both determining factors determine the range of exercises to be applied in strength training and the form of performance. The greatest training potential is found in the most specific exercises. The problem of training is not solved by performing many and very varied exercises, but by using those that have a more direct influence on performance.</p>
<p>Other questions such as what are the limiting factors from the point of view of strength performance, as can occur in endurance sports, or what is the need to maintain a certain degree of strength during the competitive phase, the number of competitions that have to be held and the distribution of them, what are the strengths and weaknesses of the athlete or what role the athlete plays in the case of team sports, must also be taken into account before making decisions about the work to be done.</p>
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		<title>Training cycles and their 5 phases</title>
		<link>https://fitenium.com/en/training-cycles-and-their-5-phases/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=training-cycles-and-their-5-phases</link>
		
		<dc:creator><![CDATA[Adrian Garcia]]></dc:creator>
		<pubDate>Fri, 28 Jul 2023 12:39:21 +0000</pubDate>
				<category><![CDATA[Character of the effort]]></category>
		<category><![CDATA[strength training]]></category>
		<category><![CDATA[intensity]]></category>
		<category><![CDATA[training cycle]]></category>
		<category><![CDATA[volume]]></category>
		<guid isPermaLink="false">https://fitenium.com/training-cycles-and-their-5-phases/</guid>

					<description><![CDATA[Training cycles and their 5 phases The training cycles are training time slots in which all the necessary loads have been applied, according to the programmer&#8217;s criteria, to achieve the expected objective. In this series of articles we deal with...]]></description>
										<content:encoded><![CDATA[<h2><strong>Training cycles and their 5 phases</strong></h2>
<p>The training cycles are training time slots in which all the necessary loads have been applied, according to the programmer&#8217;s criteria, to achieve the expected objective.</p>
<p><em>In this series of articles we deal with some of the most important concepts of strength training, collecting notes from the recently published book <a href="https://www.libreriadeportiva.com/libro/fuerza-velocidad-y-rendimiento-fisico-y-deportivo_73450"><strong>Strength, Speed ​​and Physical and Sports Performance</strong></a> written by renowned researchers <strong>Juan José González Badillo and Juan Ribas Serna.</strong></em></p>
<h3>SUMMARY</h3>
<ul>
<li><em>Each training cycle will be made up of 5 phases in which a maximum volume will be reached with a minimum intensity in the first phase, and the volume will gradually decrease and intensity increase during the cycle until concluding with recovery in the last phase.</em></li>
<li><em>It has been possible to establish that the training volume has a certain individual value for each athlete, above and below which the best results are not obtained.</em></li>
<li><em>If strength and endurance are trained separately, especially every other day on every other day, both can be improved.</em></li>
</ul>
<p>&nbsp;</p>
<p>The permanent objectives throughout the training cycles will be the improvement of the applied force (improvement of speed) before any load and, especially, before the competition load (improvement of useful force), as well as the corresponding improvement of production. force in unit time (RFD) and specific RFD.</p>
<p>During the duration of the cycle, the evolution of intensity and volume occurs, as well as the exercises used. The evolution of these variables is continuous, so the inclusion of a series of &#8220;phases&#8221; within a cycle only makes sense if it is done with the aim of giving guidance on the moment of evolution of these variables.</p>
<p>The “moment” is defined by the volume and intensity values. For a better definition of the cycle, we will add its duration, generally indicating the number of weeks it comprises. When the strength needs are high, the values ​​of the intensities and volumes are the highest that can (should) be programmed, and therefore there are more differences between the different moments or &#8220;phases&#8221; of the cycle. The opposite occurs when force requirements are low.</p>
<h4>When strength needs are high, the values ​​of intensities and volumes are the highest that can (should) be programmed</h4>
<p>Therefore, the way of developing each of the training cycles is determined especially by the intensity and volume, which will be different depending on the characteristics of the sports or sports specialties and the characteristics of the subjects. The following are the basic characteristics of the different &#8220;phases&#8221; of a training cycle. These phases could be the following:</p>
<h5><strong><img class="aligncenter wp-image-30116 size-full" src="https://fitenium.com/wp-content/uploads/2020/09/OYGcUSPeJVk.jpg" alt="The training cycles" width="640" height="427">First phase:</strong></h5>
<p><em>Priority objective:</em> to improve the maximum force applied before any load and the RFD before the training exercises. This should have a positive effect on the useful force: force applied before the load and gesture typical of the competition</p>
<p><em>Basic training:</em> the highest number of repetitions per series of the entire cycle and <a href="https://fitenium.com/pistacho-beneficios-y-caracteristicas/">character of effort (CE)</a> from low to high, depending on the strength needs and experience of the subject. It is in this phase that the volume values ​​are highest and the intensity is lowest (actual percentage of 1RM lower or velocity of the first repetition higher). The volume tends to increase in the first weeks, reaching the maximum values ​​of the entire cycle per unit of training and weekly. The loss of speed in the series will be the lowest of the entire cycle: the lower the relative intensity, the less the <a href="https://fitenium.com/la-perdida-de-velocidad-en-la-serie/">loss of speed in the series should be.</a></p>
<p>In order to make this description of the phases clearer, the classification of the CE grades in relative terms is considered. When it is said, for example, that the CE is &#8220;high&#8221;, it should be interpreted as the maximum or almost maximum requirement for the subject or the specialty to which the training is applied, but not in absolute terms. In other words, both subjects with high strength needs and those with low needs will be able to reach “their high EC” in this phase, but it will not mean the same degree of effort for everyone. In the event that the speed of movement of <a href="https://fitenium.com/la-velocidad-de-ejecucion/">the load could be measured, the CE</a> will be determined by the speed of the first repetition and by the loss of speed in the series.</p>
<h5><strong>Second level:</strong></h5>
<p><em>Priority objective:</em> improve the maximum force applied to any load, the RFD and the specific force: peak force and specific RFD (useful force)</p>
<p><em>Basic training: decrease the number of repetitions per set, increase the actual percentage of the RM, or decrease the speed of the first repetition</em></p>
<p><em>Increased speed loss in the series.</em></p>
<h5><strong>Third phase:</strong></h5>
<p><em>Priority objective:</em> improve or at least maintain the maximum force applied to any non-specific load and improve the specific RFD and peak force (useful force). ,</p>
<p><em>Basic training</em><em>:</em> reducing the number of repetitions per series. Increase in the actual percentage of the RM or decrease the speed of the first repetition. Maximum or almost maximum loss of speed in the series of the cycle. There can be an oscillation of these variables between sessions, applying light loads in some sessions in the event that an exercise was done three times a week.</p>
<p><strong><img class="aligncenter wp-image-29642 size-full" src="https://fitenium.com/wp-content/uploads/2020/09/TAZoUmDqzXk.jpg" alt="training phases" width="640" height="960"></strong></p>
<h5><strong>Fourth phase</strong></h5>
<p><em>Priority objective:</em> improve the specific RFD and peak force and at least maintain the maximum applied force under any non-specific load</p>
<p><em>Basic training:</em> reducing the number of repetitions per series. Increase in the actual percentage of the RM or decrease the speed of the first repetition. Maximum loss of speed in the series within the cycle. There can be an oscillation of these variables between sessions, applying light loads in some sessions in the event that an exercise was done three times a week.</p>
<p>In addition to the time dedicated to this type of load, training of these characteristics can be extended in a maintenance phase for two or three weeks.</p>
<h5><strong>Fifth Phase</strong></h5>
<p><em>Priority objective:</em> priority: recover.</p>
<p><em>Basic training</em>: very little or no typical strength training.</p>
<p><em>Duration:</em> between 1 and 4 weeks, depending on the time of the season.</p>
<p>The duration of the complete cycle should not exceed 12-14 weeks. The most frequent length could be between 8 and 10 weeks, although cycles of 4 to 6 weeks are also very effective and sometimes necessary. Other shorter cycles may serve to maintain or recover, or at least to approach recently achieved levels of strength performance.</p>
<p>In strength work, one cannot currently speak of programming with an annual cycle. The number of cycles can range from three to five-six, depending on the competition system. In these circumstances, each one of them would have a different character, giving greater emphasis to the volume and the first phases of the cycle in some cases, and accentuating the intensity in others.</p>
<h4><strong>It has been possible to establish that the training volume has a certain individual value for each athlete, above which the best results are not obtained.</strong></h4>
<p>If we assume that more than one cycle per year is carried out, the evolution of the volume within each cycle and between them through the years should be approximately as follows:</p>
<ul>
<li>In the first years (2-3) of strength training, the volume achieved each cycle is increasing.</li>
<li style="text-align: left;">The <a href="https://fitenium.com/progresion-en-el-entrenamiento/">progression</a> of the volume within each cycle is faster and faster, as the maximum volume of each of them increases. For this reason, the maximum value of the volume is reached earlier and the progression time in each cycle is shorter.</li>
<li>The volume decrease begins earlier and earlier with respect to the competition date within each cycle. The higher the maximum volume reached, the longer the recovery phase.</li>
</ul>
<p>After these first seasons of strength training, the dynamics of the volume is quite stable, its modifications depending on the most important competition dates. If you keep track of the load that each athlete performs, you could establish the optimum volume of work for each one, or at least detect when a load begins to be ineffective, since <strong>It has been possible to establish that the training volume has a certain individual value for each athlete, above and below which the best results are not obtained.</strong>.</p>
<p>Although it is possible that you will never know exactly what these optimal levels are, it is convenient to have an approximate idea of ​​the maximum load that a subject or a group of athletes can bear that reduces individual and collective performance within a specialty. sporty.</p>
<h4><img class="aligncenter size-full wp-image-29402" src="https://fitenium.com/wp-content/uploads/2020/09/8hzXSyeAirU.jpg" alt="" width="640" height="427"></h4>
<h4>The youngest would perform between three and four cycles a year</h4>
<p>In the first years it is more important that all the phases of each complete cycle are completed than to subordinate them to possible competitions. <strong>It would be convenient for the youngest to carry out three-four cycles a year,</strong> without excessively modifying this structure when it was necessary to adapt it to the competitions.</p>
<p>The dynamics of the volume per week can be in very different ways and quite irregular at times, although some basic rules can be given that quite likely ensure good performance in strength. <strong>The week is usually taken as a training unit and it is quite comfortable to organize the load distribution,</strong> although the smallest load units are not always a week, but a few days. In particular, we will talk about the load corresponding to the strength work that must go in coordination with the rest of the training.</p>
<p>If we find ourselves in a phase in which the fundamental objective is the improvement of strength performance, the global load should be subordinated to the training of this quality. If, on the contrary, it is intended to maintain performance, the strength work will not be very demanding and will not significantly influence the overall organization of the load, so it will not influence other training modalities and objectives.</p>
<h4><strong>if strength and endurance are trained separately, especially on alternate days on alternate days, both can be improved</strong></h4>
<p>According to these premises, the dynamics of the force will be in some moments different from the dynamics of the general charge, which is correct and necessary. It is possible to consider different weekly or monthly objectives with specific activities for each one without serious interference, although the performance is not as high as if we only worked for one of them.</p>
<p>Perhaps the greatest incompatibility could occur between strength training and resistance training, but it is known that <strong>if these qualities are trained separately, especially on alternate days on alternate days, both can be improved</strong> at a level sufficient for the demands of sports that do not require the development of any of them to a very high degree. In any case, always the resistance would benefit more from the strength than the strength of the resistance.</p>
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		<title>Training density</title>
		<link>https://fitenium.com/en/training-density/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=training-density</link>
		
		<dc:creator><![CDATA[Adrian Garcia]]></dc:creator>
		<pubDate>Fri, 28 Jul 2023 11:58:29 +0000</pubDate>
				<category><![CDATA[Fatigue]]></category>
		<category><![CDATA[strength training]]></category>
		<category><![CDATA[Training]]></category>
		<category><![CDATA[fatigue]]></category>
		<category><![CDATA[intensity]]></category>
		<category><![CDATA[metabolic stress]]></category>
		<category><![CDATA[training density]]></category>
		<category><![CDATA[volume]]></category>
		<guid isPermaLink="false">https://fitenium.com/training-density/</guid>

					<description><![CDATA[Training density The density is expressed by the relationship between the total work or the number of repetitions performed and the time spent on it. In this sense, it is identified with a way of expressing the overall mechanical power...]]></description>
										<content:encoded><![CDATA[<h2>Training density</h2>
<p><strong>The density is expressed by the relationship between the total work or the number of repetitions performed and the time spent on it.</strong> In this sense, it is identified with a way of expressing the overall mechanical power of a training unit. Density is primarily determined by the recovery time between reps and sets, although it also extends to recovery between sessions and between full training cycles.</p>
<p><em>In this series of articles we deal with some of the most important concepts of strength training, collecting notes from the recently published book <a href="https://www.libreriadeportiva.com/libro/fuerza-velocidad-y-rendimiento-fisico-y-deportivo_73450"><strong>Strength, Speed ​​and Physical and Sports Performance</strong></a> written by renowned researchers <strong>Juan José González Badillo and Juan Ribas Serna.</strong></em></p>
<h3>Summary</h3>
<ul>
<li><em>Training density is the relationship between the volume of training and the time spent doing it.</em></li>
<li><em>A higher density tends to produce greater metabolic stress and greater fatigue.</em></li>
<li><em>High intensities and densities of training, which are probably only necessary —if they are at all— for advanced athletes, have a limit from which their use can be negative.</em></li>
</ul>
<p>The recovery time comes to complete the characteristics of the intensity of the training. <strong>Thus, given the same intensity and <a href="http://componentes-de-la-carga-en-el-entrenamiento-volumen">volume</a>, the greater the density of the training carried out, the greater the overall mechanical power and, therefore, the <a href="https://fitenium.com/componentes-de-la-carga-la-intesidad/">greater the overall intensity</a> of the training.</strong> The effects and importance of recovery go beyond the training session to influence the entire cycle, the relationship between cycles within a season and even between seasons.</p>
<p>&nbsp;</p>
<h4><img class="aligncenter wp-image-30396 size-full" src="https://fitenium.com/wp-content/uploads/2020/09/wxx_3ooXW70.jpg" alt="Sports training volume" width="640" height="427"></h4>
<h4></h4>
<h4><strong>The density is expressed by the relationship between the total work or the number of repetitions performed and the time spent on it.</strong></h4>
<p>On many occasions, the best solution to achieve a clear improvement in performance, whether there has been a phase of overtraining (stagnation or setback in results) or not, is a long recovery period without training, rest (González- Badillo, 1991, p.100).</p>
<p><strong>The higher or lower density within an exercise or session can influence the training effect, since a lower recovery between series, that is, a higher density, tends to produce greater metabolic stress and greater fatigue.</strong>, which must be taken into account as a determining factor of the overall load used and the effect expected from the training. Therefore, the training density can be considered as a complement to the other intensity criteria, but, although it is generally subordinated to the objectives defined by the other criteria, it must be taken into account as a possible determining element of the load magnitude. .</p>
<p>As occurs with the training volume as a component of the load, studies have also been carried out on intensity aimed at determining the degree of intensity or load and physical and sports performance. Making a synthesis of a good part of them, we highlight some data below.</p>
<p>The problem of optimal load and stimulus effectiveness within the training process is not satisfactorily resolved (Pampus et al., 1990). <strong>The importance of the optimal training load is justified by the small differences in performance between winners and losers in a competition</strong> (Kuipers, 1998), <strong>although there is very little scientific data on optimal training to reach maximum performance</strong> (Kuipers, 1998). , nineteen ninety six).</p>
<p>&nbsp;</p>
<h4><img class="aligncenter wp-image-29774 size-full" src="https://fitenium.com/wp-content/uploads/2020/09/DBhIAuHyjPM.jpg" alt="training density" width="640" height="360"></h4>
<h4></h4>
<h4>The importance of the optimal training load is justified by the small differences in performance between winners and losers in a competition.</h4>
<p><strong>While the rapid or immediate improvement in performance may be directly related to intensity, the final level of performance is inversely related to training intensity</strong> (Edington and Edgerton, 1976; in Stone et al. 1991). In this sense, it has been verified that if an athlete tends to perform the greatest possible number of repetitions with intensities greater than 90% of his RM, he does not achieve the best results (González-Badillo et al. 2006), as well as that the number of repetitions with intensities greater than 90% of the RM does not have a positive linear relationship with the results (González-Badillo et al. 2006).</p>
<p>The explanation for this lack of linearity when maximum achievable values ​​of maximum intensities are reached could lie in the lack of positive response of the organism to an excess of stimulation and a high training density. This has been concluded in some studies.</p>
<p>For example, it is stated that the coincidence of very high values ​​of volume at the moment in which a high intensity is also performed is very likely to lead to overtraining (Kraemer et al. 1995), or that using 70 repetitions per week with intensities 100% of 1RM led to overtraining (decreased squat result), while performing 40 repetitions per week with intensities close to maximum (95%) produced improvements in 1RM (Fry et al, 1994).</p>
<p>&nbsp;</p>
<h4><img class="aligncenter wp-image-29474 size-full" src="https://fitenium.com/wp-content/uploads/2020/09/REafAGAia54.jpg" alt="sports training load" width="640" height="960"></h4>
<h4></h4>
<h4>It has been proven that if an athlete tends to perform as many repetitions as possible with intensities greater than 90% of his RM, he does not achieve the best results.</h4>
<p>From the study by Medvedev and Dvorkin (1987) it can be deduced that <strong>the optimum percentage of 1RM for strength improvement is not the same for all ages and performance levels.</strong> Indeed, in this study it was observed that the youngest subjects, 13-14 years old and 15-16 years old, improved more with mean percentages of 70 and 80%, respectively, with those who did 3-4 repetitions per series, which using 90% percentages with 1-2 repetitions per set.</p>
<p>It therefore seems reasonable that we should get the most out of each range of percentages before using the highest percentages. Even in the older group of athletes (17-20 years) in this same study, 80% produced in the long run (at the end of the 6 and 8 months that the study lasted) better effects than 90%.</p>
<p>These results seem to justify Edington and Edgerton&#8217;s (1976; in Stone et al. 1991) suggestion noted above that while rapid/immediate performance improvement may be directly related to intensity, the <strong>final level of performance is inversely related to training intensity </strong>(and in turn the training density), and also with the conclusions of Fry (1998), which indicates that <strong>the use of maximum intensities (1RM) can be satisfactory in a short space of time, but the continued use of these training units will frequently be negative to continue improving</strong>, y Fry et al. (2000), when they conclude that even in the event that the continued use of this work system does not produce a decrease in the result in the trained exercise (squat), it can be counterproductive in other performances such as speed (Fry et al. ., 2000).</p>
<p>&nbsp;</p>
<h4><strong><img class="aligncenter wp-image-29332 size-full" src="https://fitenium.com/wp-content/uploads/2020/09/D066IGZpo7Y.jpg" alt="components of a workout" width="640" height="321"></strong></h4>
<h4><strong>the use of maximum intensities (1RM) can be satisfactory in a short space of time, but the continued use of these training units will frequently be negative to continue improving</strong></h4>
<p>Therefore, <strong>if a high intensity is maintained for a long time (90% for 6-8 months, in the study by Medvedev and Dvorkin, 1987), the results tend to decrease, and may even lead to overtraining, or, at best, cases, the results would be lower than those obtained with medium intensities</strong>.</p>
<p>These results are in the same line as those obtained by González-Badillo (González-Badillo et al., 2006), in which it was observed that the relationship between the number of maximum repetitions (90% and more of the RM) and the results is curvilinear, or with the results of Busso (2003), who finds a curvilinear relationship between the daily training load and the gain in performance.</p>
<p><strong>This suggests that these high training intensities and densities, which are probably only necessary —if at all— for advanced athletes, have a limit beyond which their use can be negative.</strong></p>
<p>In addition, this limit is not determined by the athlete&#8217;s own ability to carry out training sessions with these intensities, since <strong>those subjects who performed the greatest possible number of repetitions with more than 90% did not achieve the best, observing that these intensities did not have a positive linear relationship with the improvement of the marks</strong> (González-Badillo et al., 2006).</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
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		<title>Definition of sports training load</title>
		<link>https://fitenium.com/en/definition-of-sports-training-load/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=definition-of-sports-training-load</link>
		
		<dc:creator><![CDATA[Adrian Garcia]]></dc:creator>
		<pubDate>Fri, 28 Jul 2023 11:47:34 +0000</pubDate>
				<category><![CDATA[Training]]></category>
		<category><![CDATA[training schedule]]></category>
		<category><![CDATA[intensity]]></category>
		<category><![CDATA[training load]]></category>
		<category><![CDATA[volume]]></category>
		<guid isPermaLink="false">https://fitenium.com/definition-of-sports-training-load/</guid>

					<description><![CDATA[Definition of sports training load In this article, a definition of the sports training load is made in order to understand what loads we should apply in training. In this series of articles we deal with some of the most...]]></description>
										<content:encoded><![CDATA[<h2>Definition of sports training load</h2>
<p>In this article, a definition of the sports training load is made in order to understand what loads we should apply in training.</p>
<p><em>In this series of articles we deal with some of the most important concepts of strength training, collecting notes from the recently published book <a href="https://www.libreriadeportiva.com/libro/fuerza-velocidad-y-rendimiento-fisico-y-deportivo_73450"><strong>Strength, Speed ​​and Physical and Sports Performance</strong></a> written by renowned researchers <strong>Juan José González Badillo and Juan Ribas Serna.</strong></em></p>
<h3><strong>Summary </strong></h3>
<ul>
<li><em>The load is the set of biological and psychological demands caused by training activities.</em></li>
<li><em>The coach&#8217;s task is to define the load accurately and comprehensively and to control and analyze the relationship between the actual load and the proposed load as well as the performance.</em></li>
<li><em>The more the volume, intensity and exercise are adjusted to the characteristics of the competition, the more specific the training load will be.</em></li>
</ul>
<p>&nbsp;</p>
<p>According to the elementary principle of adaptation of living beings to the demands of the environment, the following sequence can be applied:</p>
<p style="padding-left: 40px;">1/ Current situation of the subject</p>
<p style="padding-left: 40px;">2/ Application of the appropriate stimulus (load)</p>
<p style="padding-left: 40px;">3 / Improved performance.</p>
<p>That is to say, the application of a load or stimulus that adjusts to the initial situation of the subject, referring to his work capacity, performance obtained to date, experience and objectives that are intended, must produce the desired effects.</p>
<p>&nbsp;</p>
<p><img class="aligncenter wp-image-30134 size-full" src="https://fitenium.com/wp-content/uploads/2020/09/Na20SZHH36I.jpg" alt="Training Load Definition" width="640" height="381"></p>
<p>Load is understood as the set of biological and psychological demands caused by training activities. In the concept of load we distinguish two variants: real load and proposed load The real load must be understood as the <a href="https://fitenium.com/el-caracter-esfuerzo-fisico/">degree of effort</a> that is programmed, which is manifested by the set of biological and psychological demands caused by training activities, which is expressed by wear / different physiological alterations / alteration of the homeostatic balance.</p>
<h3>Load is understood as the set of biological and psychological demands caused by training activities.</h3>
<p>This wear and alterations reflect the degree of effort made by the subject. By proposed load we must understand the set of stimuli expressed in the form of training (series, repetitions, times, speeds, distances, recovery breaks&#8230;). It is the cause of functional, biochemical, morphological and physical modifications.</p>
<p>The load that is programmed is the actual load, which is identified as the degree of effort programmed. This effort must be adequately expressed through the proposed load. That is to say, the programmed effort must be expressed in series, repetitions, weights, pauses, speeds&#8230;, which accurately reflect the real load and which cause the programmed effort. The precision and adjustment of the real load and the interrelation of this with the proposed load constitutes the essence of sports training.</p>
<p>Therefore, <strong>the training load initially presents two basic questions:</strong> </p>
<p style="padding-left: 40px;">a) Is the actual programmed load correct?</p>
<p style="padding-left: 40px;">b) Is the actual scheduled load well represented by the proposed load?</p>
<h3><img class="aligncenter wp-image-29892 size-full" src="https://fitenium.com/wp-content/uploads/2020/09/b4GeymbJl8.jpg" alt="what is the load in sports training" width="640" height="960"></h3>
<h3>The precision and adjustment of the real load and the interrelation of this with the proposed load constitutes the essence of sports training.</h3>
<p>This means that the fundamental task of the coach and of the training methodology is twofold: 1) define the load precisely and exhaustively and ii) control and analyze the relationship between the actual load and the proposed load and between both and performance. These tasks entail another relevant problem, which is determining how to measure and quantify the actual and proposed load, and how to validate models for measuring and quantifying the loads. The charge is defined by its magnitude and by its degree of specificity.</p>
<p>The magnitude of the load depends on the degree of stimulus that the load supposes. A stimulus is an agent that produces a functional reaction in the organism, and has two elements that determine its magnitude:</p>
<p>1) the amplitude, which would be represented by the tension or force in each unit of action, and</p>
<p>2) time, which determines the duration of the amplitude or voltage.</p>
<p>The amplitude element comes to represent the intensity component of the stimulus or charge, and the time is representative of the volume of the charge. If we consider the magnitude of the stimulus as the product of amplitude (intensity) and time (volume), different combinations of amplitude and time could give rise to the same magnitude, but stimuli with different characteristics.</p>
<p>For example, lifting 80 kg (<a href="https://fitenium.com/componentes-de-la-carga-la-intesidad/">intensity</a>) 10 times (<a href="http://componentes-de-la-carga-en-el-entrenamiento-volumen">volume</a>) would give us a magnitude of 800, which would be the same stimulus magnitude as lifting 10 kg 80 times, but naturally the characteristics of the stimulus and its effects are different. That is, the same numerical quantity can represent two functionally different stimuli. A third component that determines the magnitude of the stimulus is the exercise with which the training is carried out, in such a way that the same load (weight or intensity) lifted the same number of times (volume) can suppose a stimulus of different magnitude if the This exercise consists, for example, in bending the elbow or doing deep leg flexions.</p>
<h3><img class="aligncenter wp-image-29836 size-full" src="https://fitenium.com/wp-content/uploads/2020/09/tc1eFr68CTU.jpg" alt="what is the load in sports training" width="640" height="427"></h3>
<h3>the more the volume, intensity and exercise are adjusted to the characteristics of the competition, the more specific the load will be</h3>
<p>Depending on the degree of volume and intensity, the stimulus can have three basic objectives: maintain the performance obtained, improve performance or recover the body. If the application of recovery-oriented stimuli is prolonged, the effect is detraining. If the stimulus aimed at improving performance is excessive and continuous, the effect will be negative and stagnation or loss of performance will occur.</p>
<p>The specificity is determined by the degree to which the load approximates the dynamic, kinematic and metabolic characteristics of the competition.</p>
<p>This means that the more the volume, intensity and exercise are adjusted to the characteristics of the competition, the more specific the load will be. In this case, volume, intensity, and exercise are considered specific. But if only one of these elements is not specific, the load as a whole will no longer be specific.</p>
<p>For example, a 100-meter runner can do the running exercise as training, which would be a specific element, but if the speed is very slow and is maintained for a long period of time, the training, despite having the exercise as a specific element, it will be very negative and therefore non-specific.</p>
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		<title>Components of training load: volume</title>
		<link>https://fitenium.com/en/components-of-training-load-volume/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=components-of-training-load-volume</link>
		
		<dc:creator><![CDATA[Adrian Garcia]]></dc:creator>
		<pubDate>Fri, 28 Jul 2023 11:33:21 +0000</pubDate>
				<category><![CDATA[Fatigue]]></category>
		<category><![CDATA[Training]]></category>
		<category><![CDATA[training schedule]]></category>
		<category><![CDATA[intensity]]></category>
		<category><![CDATA[training]]></category>
		<category><![CDATA[volume]]></category>
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					<description><![CDATA[Components of training load: volume Within the components of the training load, the volume represents the amount of work, the amount of training, the number of actions performed&#8230;, which is expressed differently depending on the type of training performed. The...]]></description>
										<content:encoded><![CDATA[<h2>Components of training load: volume</h2>
<p>Within the components of the training load, the volume represents the amount of work, the amount of training, the number of actions performed&#8230;, which is expressed differently depending on the type of training performed. The volume is determined and modified by the duration and frequency of the activity. Basically the volume represents the &#8220;time&#8221; factor of the magnitude of the stimulus and has little or no meaning if it is not accompanied by the intensity component.</p>
<p><em>In this series of articles we deal with some of the most important concepts of strength training, collecting notes from the recently published book <a href="https://www.libreriadeportiva.com/libro/fuerza-velocidad-y-rendimiento-fisico-y-deportivo_73450"><strong>Strength, Speed ​​and Physical and Sports Performance</strong></a> written by renowned researchers <strong>Juan José González Badillo and Juan Ribas Serna.</strong></em></p>
<h3>Summary</h3>
<ul>
<li><em>Volume represents the number of repetitions performed in a workout.</em></li>
<li><em>The volume has a limited meaning if it is not accompanied by the intensity with which it has been performed.</em></li>
<li><em>The intensity with which each repetition is executed is the main determinant of the training effect, therefore, the adequate volume should be conditioned by the intensity.</em></li>
<li><em>The best training effect will be achieved when a program is designed with alternating high, medium and light volumes, in the appropriate order and magnitude without exceeding the total volume considered maximum.</em></li>
</ul>
<p>&nbsp;</p>
<p>When it comes to so-called &#8220;strength&#8221; training, as well as throwing and jumping, volume is measured by the number of repetitions performed. However, the value of the isolated volume is insufficient to define the load: two volumes can be two totally different workouts if the intensity is different to define the load, it is always necessary to add the corresponding intensity value to the volume value.</p>
<p>&nbsp;</p>
<p><img class="aligncenter wp-image-30328 size-full" src="https://fitenium.com/wp-content/uploads/2020/09/b5boYWIR2vU.jpg" alt="Components of training load: volume" width="640" height="958"></p>
<p>When dealing with volume as a component of load, it is important to ask ourselves a key question:</p>
<h3>how much volume should be used?</h3>
<p>This question does not have a single answer, much less a precise and universal one, but finding or coming close to an answer to this question is a permanent goal for anyone dedicated to training effect programming and analysis.</p>
<p>Some guidelines can be given that should serve as support to justify when the volume reached may be sufficient, but, as we have indicated, <strong>the volume has hardly any meaning by itself if it is not going to</strong><br />
<strong>together with the intensity factor</strong>, and therefore, in many situations, the volume will be determined by <a href="https://fitenium.com/componentes-de-la-carga-la-intensidad/">intensity</a> indicators such as, especially, the <a href="https://fitenium.com/la-velocidad-de-ejecucion/">maximum possible speed of execution.</a></p>
<h3>8 Conclusions on the volume effect collected in some investigations</h3>
<p>1 &#8211; The maximum achievable volume (maximum volume that the subject could do without immediate damage or extreme exhaustion) does not produce the best results (González-Badillo et al., 2005).</p>
<p><strong>2 &#8211; A volume of 85% of the maximum achievable allowed to obtain the best results</strong> (<a href="https://pubmed.ncbi.nlm.nih.gov/16095427/">González-Badillo et al., 2005).</a> With a volume of approximately 65% ​​of the maximum achievable, the same results were obtained as with said maximum and probably the improvement in the exercises with the highest speed of execution are more affected by the size of the volume (González-Badillo et al., 2005). .</p>
<p><strong>3 &#8211; Three sets per muscle group per week to failure produce the same or greater effects than 6 and 12 sets</strong> <a href="https://journals.lww.com/nsca-jscr/Abstract/1997/08000/The_Effect_of_Weight_Training_Volume_on_Hormonal.3">(Ostrowski et al., 1997).</a></p>
<p>4 &#8211; The conclusion of some studies and reviews indicate that there is little scientific evidence and <strong>there is no physiological theoretical basis to suggest that a greater volume of practice provides a greater increase in strength</strong> (Carpinelli and Otto, 1998).</p>
<p>5 &#8211;  <strong>The improvement in athletic performance appears to be related to progression to higher volume with increased strength training experience.</strong> (ACSM&#8217;s position stand, 2002), but the use of high training loads is not based on the idea that “the more the better” (Viru, 1993), because the key to success does not seem to lie in an extreme volume of training ( Smirnov, 98). For example, doubling the training volume of a group of swimmers for six weeks was found not to result in increased performance (Costill et al. 1991), and the effectiveness of training volume appears to decrease progressively as the athlete&#8217;s performance increases. (Matveyer and Gilyasova, 1990).</p>
<p>&nbsp;</p>
<p><img class="aligncenter size-full wp-image-30178" src="https://fitenium.com/wp-content/uploads/2020/09/1JctMdAII9M.jpg" alt="" width="640" height="480"></p>
<p>6 &#8211; In order to verify where the optimal training volume could be, 140 studies related to the effect of the number of series performed per exercise were analyzed, 1433 effect size values ​​were calculated to be able to compare the effect of each of the studies, and it was concluded that <strong>when more than 3-4 sets per exercise were done, the training effect began to diminish, both in trained and untrained subjects</strong> (Rhea et al., 2003). In other words, once a certain volume is reached, increasing it does not seem to produce a greater improvement, and may even reduce performance.</p>
<p>7- In several recent studies it has been observed that <strong>losing 10 or 20% of the speed reached in the first repetition in the series produces better results than continuing to do repetitions in the series until losing between 30% or 40-50% of the speed i</strong>initial (Pareja-Blanco et al., 2017; Rodríguez-Rosell et al., 2018; Rodríguez-Rosell et al., 2019). When 10 and 20% of the speed of the first repetition was lost, the volume was 46% and 64% of that performed when 40-50% was lost.</p>
<p>8 &#8211; Therefore, studies indicate that <strong>there seems to be an optimal zone of amount of training that provides a greater increase in results. However, this optimal zone is poorly defined, and exceeding it can lead to overtraining syndrome.</strong> (Lehmann et al. 1993; in Kuipers, 1996). |</p>
<p>Although it is necessary to find the right volume of training, but the questions are numerous:</p>
<ul>
<li>How to find the right volume and how to know that it is?</li>
<li>what is the appropriate volume of a session?</li>
<li>does it serve us forever?</li>
<li>Is it the same for all subjects who pursue the same objective in the same specialty?</li>
<li>What period of time are we referring to when we speak of adequate volume?</li>
</ul>
<p>These questions do not have and probably will never have a precise and definitive answer, but given the importance of this factor for the best driving in the sporting manner, some practical guidelines can be given that can serve as a reference and support when making decisions. decisions.</p>
<p>&nbsp;</p>
<h4><img class="aligncenter wp-image-30070 size-full" src="https://fitenium.com/wp-content/uploads/2020/09/jO6vBWX9h9Y.jpg" alt="load components in sports training" width="640" height="427"></h4>
<h4></h4>
<h3>How to find the right volume and how to know that it is?</h3>
<p>The most appropriate procedure is the use of the experimental method, through which the variables we want to study can be manipulated, which in this case would be using different volume values, and controlling other variables that could influence the results, such as intensity and the type of exercise, mainly.</p>
<p>In this way, the cause-effect relationship between volume and performance could be verified. Given the difficulty involved in carrying out studies of this type, especially with subjects who compete officially, the second alternative is the observation and continuous monitoring of training loads and their effects. This route is more accessible, although less precise, to achieve these objectives.</p>
<p>This observation must focus on the analysis of two main sources of information: physiological sources such as cardiorespiratory, hormonal, enzymatic, metabolic&#8230; responses to effort, and <strong>mechanical sources, more accessible and probably accurate, such as the evolution of the maximum speed and the loss of speed in the series in each training session</strong>.</p>
<p>&nbsp;</p>
<h4><img class="aligncenter wp-image-29900 size-full" src="https://fitenium.com/wp-content/uploads/2020/09/ltq_MruDgkc.jpg" alt="the volume" width="640" height="427"></h4>
<h4></h4>
<h3 style="text-align: left;">What is the appropriate volume of a session?</h3>
<p>The intensity with which each repetition is executed is the main determinant of the training effect, therefore, the adequate volume should be conditioned by the intensity, understood in this case as execution speed. The most reasonable thing is to think that as long as the intensity can be maintained, the repetition of said intensity &#8211; which constitutes the volume &#8211; could be positive.</p>
<h4></h4>
<h3 style="text-align: left;">Should all the repetitions that can be done with the planned intensity be exhausted in each session?</h3>
<p>This question leads us to a more complex problem, which would focus on determining how many times or how often the maximum possibilities of maintaining the intensity must be exhausted and how many times we must fall below it.</p>
<p>The criterion of <strong>The most rational reference to determine the end of the session or of the repetitions or series within an exercise would be the immediate response of the subject from a dynamic point of view, understood as the <span style="text-decoration: underline;">force lost in the series and</span>, especially its cinematic component understood as the <a href="https://fitenium.com/la-perdida-de-velocidad-en-la-serie/"><span style="text-decoration: underline;">loss of speed in the series</span></a></strong>. Additionally, the concentration of lactate and ammonium could also be recorded.</p>
<p>The speed losses will indicate the degree of fatigue created in the series or set of series, and the metabolites the degree of metabolic stress caused, in which the main role would be played by ammonia. The loss of speed, and, in part, the ammonium, which is determined precisely by the loss of speed, are quite reliable indicators of the degree and type of effort that the subject is making, so they can allow us to check recurrently the relationship between the <a href="https://fitenium.com/el-caracter-esfuerzo-fisico/">degree of effort</a> and the results or effects of training.</p>
<p><strong>In this way, deciding when a session or exercise should be interrupted would not only be determined by intuition, but by data that more accurately reflects the true effort made.</strong> &#8220;Intuition&#8221; is not negligible, and will always accompany the training programmer, applicator, and analyzer, but this &#8220;intuition&#8221; must ultimately be the product of one&#8217;s own experience, which will be more reliable if it is based on the information that is derived from the subject&#8217;s mechanical and physiological response to the stimulus that training entails.</p>
<h3></h3>
<h3><img class="aligncenter size-full wp-image-29776" src="https://fitenium.com/wp-content/uploads/2020/09/tBmmA3pTciA.jpg" alt="" width="640" height="960"></h3>
<h3></h3>
<h3 style="text-align: left;">Assuming the right volume has been found, does it last forever?</h3>
<p>Theoretically, at each moment of sporting life it is supposed that there must be a more adequate volume. In the early years the problem can be reasonably solved if you train with a moderate progression of the load, both in volume and intensity. The more moderate the load, as long as it produces a performance improvement, the more likely the load is appropriate. Dosage problems<br />
of the volume appear when stagnations in the results begin to occur.</p>
<p>Therefore, we can accept that <strong>in most cases adequate volume will tend to increase moderately during the first 3-5 years</strong>, and from this point on, much more emphasis should be placed on finding out what is the volume that allows a better and greater positive adaptation of the subject to the training loads. In this case, we would be in the situation described in the previous paragraphs, so it should be taken into account<br />
the considerations set forth there.</p>
<p>Also, the focus now needs to be on how many times in a year/season the proper peak volume is reached and how long peak load phases should be maintained. It should be taken into account that <strong>The fact that you get an upgrade with a considerable load does not ensure that that is the best load. It is possible that, if the same load is used again, the results are not only not positive, but may even be negative and there is even a risk of injuries directly derived from the demand of the load itself.</strong>.</p>
<p>&nbsp;</p>
<h3><img class="aligncenter size-full wp-image-29540" src="https://fitenium.com/wp-content/uploads/2020/09/E_y9X4kNzxs.jpg" alt="" width="640" height="427"></h3>
<h3></h3>
<h3 style="text-align: left;">Is the adequate volume the same for all the subjects who seek the same objective in the same specialty?</h3>
<p>Experience indicates that the answer to this question is clearly negative. Not all subjects are capable of supporting the same loads.</p>
<h3 style="text-align: left;">What is the reference time period for an adequate volume?</h3>
<p>The concept of adequate volume can be applied to any unit of training, from the volume of a series to that of a year. In order to adequately organize the training, it would be convenient for us to have a valid reference on what is the maximum-adequate volume of a session, a week, a month (four weeks) and a complete training cycle (6-12 weeks).</p>
<p><strong>The best effect will be achieved when a program is designed with correct dynamics (alternation of medium and light high volumes in the appropriate order and magnitude), without exceeding the total volume considered as the maximum achievable in each training unit and for the appropriate time. . </strong></p>
<p>&nbsp;</p>
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		<title>Strength training through speed</title>
		<link>https://fitenium.com/en/strength-training-through-speed/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=strength-training-through-speed</link>
		
		<dc:creator><![CDATA[Adrian Garcia]]></dc:creator>
		<pubDate>Fri, 28 Jul 2023 11:22:30 +0000</pubDate>
				<category><![CDATA[Character of the effort]]></category>
		<category><![CDATA[execution speed]]></category>
		<category><![CDATA[loss of speed in the series]]></category>
		<category><![CDATA[strength training]]></category>
		<category><![CDATA[intensity]]></category>
		<category><![CDATA[volume]]></category>
		<guid isPermaLink="false">https://fitenium.com/strength-training-through-speed/</guid>

					<description><![CDATA[The organization of strength training through speed In this article an analysis of the guidelines on the organization of strength training through speed is made. In this series of articles we deal with some of the most important concepts of...]]></description>
										<content:encoded><![CDATA[<h2>The organization of strength training through speed</h2>
<p>In this article an analysis of the guidelines on the organization of strength training through speed is made.</p>
<p><em>In this series of articles we deal with some of the most important concepts of strength training, collecting notes from the recently published book <a href="https://www.libreriadeportiva.com/libro/fuerza-velocidad-y-rendimiento-fisico-y-deportivo_73450"><strong>Strength, Speed ​​and Physical and Sports Performance</strong></a> written by renowned researchers <strong>Juan José González Badillo and Juan Ribas Serna.</strong></em></p>
<h4>SUMMARY</h4>
<ul>
<li><em>During training, the load can be modified to adjust the degree of effort with the programmed relative intensity.</em></li>
<li><em> For the same loss of speed in the series, in all cases we will have useful information to know with high precision what training we have done, what degree of effort</em></li>
<li><em>In training organized through speed, a determined number of repetitions in the series is not programmed, but rather a loss of speed in the series given the load or relative intensity selected.</em></li>
</ul>
<p>&nbsp;</p>
<p>First of all, it is convenient to take into account some prerequisites, taking into account that:</p>
<ul>
<li><a href="https://fitenium.com/velocidad-de-ejecucion-en-el-entrenamiento-de-fuerza/">The use of speed</a> aims to provide information on the control of the training load and its effects.</li>
<li>This information allows us to know with high precision with what relative intensity you train and with what <a href="https://fitenium.com/el-caracter-esfuerzo-fisico/">degree of effort</a> in the series, as well as what the training effect has been.</li>
<li>For this information to be useful, the movements must be performed at the maximum possible speed, although the different values ​​of execution speed are not associated with specific training objectives.</li>
</ul>
<h4><strong>Daily adjustment of the absolute training load</strong></h4>
<p>During training, it is possible to decide whether or not to modify the absolute load when the degree of effort represented by the first repetition with said load is lower or higher than the one programmed.</p>
<p>This adjustment would be logical if we want to be consistent with the programmed actual load (we are only referring to the speed of the first repetition in this case), and would consist of <strong>increase or decrease the absolute load by 1 measure necessary so that the relative intensity with which you train equals the programmed relative intensity.</strong></p>
<p><img class="aligncenter wp-image-30392 size-full" src="https://fitenium.com/wp-content/uploads/2020/09/pO3ssf5WUuk.jpg" alt="Strength training through speed" width="640" height="353"></p>
<p>This control would contribute in a precise way to carry out the programmed training and not a different one, which is already a great advance in the training methodology. But this control does not ensure the good result of the training. And this can be due to two reasons:</p>
<ol>
<li>to that we have been able to make wrong decisions when programming the effort, or</li>
<li>because the decision we&#8217;ve made—whether or not to change the absolute load—is wrong, or both.</li>
</ol>
<p>However, <strong>for the same loss of speed in the series, in all cases we will have useful information to know with high precision what training we have done, what degree of effort</strong> (relative intensity and loss of speed) and what its effects are. This will allow us to make better decisions in the immediate future based on the data and real behavior of the athletes.</p>
<p>In practice, when we observe a discrepancy between the programmed effort (only speed of the first repetition in this case) and the one that means for the subject the displacement of the absolute load that said effort represents, three situations can occur:</p>
<ol>
<li>that the load moves at a higher speed than expected,</li>
<li>moving at the expected speed, or</li>
<li>to do it at a slower speed.</li>
</ol>
<p>Naturally, in all cases three decisions can be made: maintain, raise or lower the load. But not in all cases these alternatives would be equally logical and reasonable.</p>
<h4><img class="aligncenter size-full wp-image-29776" src="https://fitenium.com/wp-content/uploads/2020/09/tBmmA3pTciA.jpg" alt="" width="640" height="960"></h4>
<h4>If the load moves faster than expected</h4>
<p>In the first case, if the absolute load moves at a higher speed than expected or programmed, it means that the subject has improved performance compared to what he had at the beginning of the training cycle, so it can be stated that the subject is training with a relative intensity lower than programmed.</p>
<p>The apparently most logical decision would be to increase the absolute load to the extent necessary to match the expected relative intensity. This would make it possible to comply in a very precise way with the programmed training (it is assumed that the loss of speed in the series would always be as expected).</p>
<p>But it is likely that when a subject clearly improves his performance after training for a few sessions, <strong>the most effective decision is to maintain the progression of the absolute loads predicted, even though the relative intensities with which he trains are less than those programmed.</strong></p>
<p>In this way, a progressive load would be maintained in absolute terms, although the relative load remained more or less stable or even tended to regress, which would indicate that the performance improvement is greater.</p>
<p>In other words, we propose that it is probable that when the improvement in performance is important, the progression of the absolute loads is sufficient, and very favourable, for the improvement of performance, although the relative intensity remains stable or even decreases progressively. This decision should be kept as long as the performance improvement is maintained.</p>
<p>In any case, the speed measurement will continue to inform us of both the performance progression and the degree of effort that has caused it.</p>
<h4>IF the load moves at the expected speed</h4>
<p>In the second case, if the load moves at the expected speed and more than 6-8 training sessions have passed, the situation becomes worrying, because this would mean that the subject has not experienced any improvement in performance.</p>
<p>In this case, it would be necessary to analyze all the possible circumstances that could explain the lack of positive response. If no reasons unrelated to the training itself are found (illness, personal problems, excessive work or study&#8230;) and the subject recovers easily from one session to another, the decision should be made to increase the load, together with the introduction of some variability, apart from the increase in volume and intensity: training frequency, some different exercise&#8230;, but if, on the contrary, they are noticeable <strong>symptoms of fatigue, the load should be reduced.</strong></p>
<h4><img class="aligncenter size-full wp-image-29380" src="https://fitenium.com/wp-content/uploads/2020/09/Yuv-iwByVRQ.jpg" alt="" width="640" height="427"></h4>
<h4>If the load moves at a lower speed than expected</h4>
<p>If, finally, the speed were lower than expected, an analysis of the possible causes would have to be carried out similar to that of the previous case. With non-training issues ruled out, it is unlikely that the reason for reduced performance is that the workload is low, so <strong>one should try to suspend the training session, or lower the absolute intensity to equal the expected relative intensity</strong>, or even lower it enough to get the speed above the programmed speed, and even take a rest or a few recovery sessions and increase the load again later.</p>
<p>What is indicated in the previous paragraphs is a great contribution of the measurement of speed: the coach knows the applied load and the effects that it produces permanently and immediately. It is the maximum and best information that a coach can aspire to in order to make informed decisions and improve his training methodology.</p>
<p>You have at your disposal, in each session, precise information on the training carried out and on the physical condition of the athlete or the person trained. This is what you need to make decisions that allow you to improve your own training as a coach and the performance of the people you train, of course, if you make the right decisions. But you will always have the information you really need to decide how to act.</p>
<p>The end result will depend on the technician&#8217;s ability to use that information.</p>
<h3><strong>The speed of the first repetition</strong></h3>
<p>Until now there has always been talk of the <a href="https://fitenium.com/velocidad-de-ejecucion-de-la-primera-repeticion/">speed of the first repetition</a> of the first series, which will indicate the relative intensity that represents the absolute load with which you train, but we have not said anything about how to manage this speed in the successive ones with the same exercise and absolute load.</p>
<p>In this sense, it should be considered that if more than one series is done, the absolute load will not change, even if the speed of the first repetition in successive series drops slightly. In addition, in each series the same loss of speed established for that session will continue to be applied, although, naturally, taking the speed of the first repetition of each series as a reference.</p>
<p>Therefore, what constitutes a training session is:</p>
<ol>
<li>an initial speed of the first repetition of the first set,</li>
<li>The maximum speed possible in the successive series and,</li>
<li>a loss of speed in the stable series during the total of the series performed.</li>
</ol>
<p>This means <strong>that the speed of the first repetition of each of the successive series should be the maximum possible</strong>, and the absolute load will not be modified if there is a slight loss of speed with respect to that of the first series.</p>
<p>It would not make sense or practical feasibility to try to adjust the loads further for several reasons:</p>
<ul>
<li>First of all, because the losses are very small between the first repetitions of each series (it can be reduced or adjusted if desired by slightly increasing the recovery times between series). These losses are greater the greater the speed loss programmed for the first series, that is, the closer we get to the maximum number of repetitions possible in the series. But this is something that is part of the characteristics of the training itself.</li>
<li>Secondly, because it is not feasible, without interfering in the training itself, to re-measure and make load changes in each series, because this would add significantly to the training load itself (greater number of repetitions and series than scheduled).</li>
<li>Thirdly, because fatigue is part of the training and determines the degree of effort, and this would be uncontrollable if the absolute load is constantly changing. If we were to make these adjustments on each set in order to train each set at the same initial speed (same relative intensity), we would also need to do it on each rep of a set, because it is clear that as we do reps on each set, the relative intensity (the degree of effort) that each repetition represents is different, since the speed decreases progressively.</li>
</ul>
<p>None of these changes seems recommendable and all are far from viable.</p>
<h3><strong><img class="aligncenter size-full wp-image-28512" src="https://fitenium.com/wp-content/uploads/2020/09/CQwNdMxwjfk.jpg" alt="" width="640" height="427"></strong></h3>
<h3><strong>Repetitions per set are not scheduled</strong></h3>
<p>It has already been discussed on a few occasions, but it is necessary to indicate it at this time.<strong> In training organized through speed, a determined number of repetitions in the series is not programmed, but rather a loss of speed in the series given the load or relative intensity selected.</strong></p>
<p>Carrying out speed-based training and programming the number of repetitions in the series is a contradiction and indicates little or no knowledge of the meaning of the already-hyped “speed-based training”. This means that not all the subjects will perform the same number of repetitions, the same volume, but the same degree of effort, which is what has been programmed and what, as it is reasonable to accept, determines the effect of the training.</p>
<p>On the contrary, if the same number of repetitions is programmed for the same relative intensity, the degree of effort will be different between the subjects.</p>
<h5><strong>Pre-training assessment</strong></h5>
<p>The evaluation prior to the start of a <a href="https://fitenium.com/los-ciclos-del-entrenamiento-y-sus-5-fases/">training cycle</a> is carried out through a test with progressive loads.</p>
<p>What must be measured is the average propulsive velocity with which the subjects move each load. The maximum load reached in the test is a relative load (determined by speed) equal to or slightly higher than the maximum to be used in training. It will never be necessary to measure the MRI.</p>
<p>The evaluation after the training is carried out by analyzing the changes in the average propulsive velocity before the same absolute loads as in the initial test. <strong>If the speed with the maximum absolute load in the final test is clearly higher than that obtained in the initial test, an extra absolute load can be measured that could serve as a reference for the following training cycle.</strong> But this extra load would never be included in the evaluation of the evaluated training cycle, because it is not a load common to the two tests.</p>
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		<title>If you are a beginner, start by choosing your training routine</title>
		<link>https://fitenium.com/en/if-you-are-a-beginner-start-by-choosing-your-training-routine/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=if-you-are-a-beginner-start-by-choosing-your-training-routine</link>
		
		<dc:creator><![CDATA[Pedro Lopez]]></dc:creator>
		<pubDate>Thu, 27 Jul 2023 10:43:54 +0000</pubDate>
				<category><![CDATA[routines]]></category>
		<category><![CDATA[Training]]></category>
		<category><![CDATA[definition]]></category>
		<category><![CDATA[repetitions]]></category>
		<category><![CDATA[volume]]></category>
		<category><![CDATA[workout]]></category>
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					<description><![CDATA[&#160; If you don&#8217;t already know Fitenium is a free, mobile, video-based social network for athletes who train strength or bodyweight exercises. At Fitenium users can follow their performance, compete and get discounts in nutrition and sports equipment stores. Download...]]></description>
										<content:encoded><![CDATA[<p>&nbsp;</p>
<p><em>If you don&#8217;t already know Fitenium is a free, mobile, video-based social network for athletes who train strength or bodyweight exercises. At Fitenium users can follow their performance, compete and get discounts in nutrition and sports equipment stores. <a href="https://play.google.com/store/apps/details?id=com.fiteniummobile">Download it here.</a></em></p>
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<p>Choosing the first routine when you arrive at the gym or when you start training is not easy, but it can impact your future performance and will save you from losing a lot of time, fatigue and even motivation. It is the most important decision when you start.</p>
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<p>Next, we will try to shed some light on the most basic question when we first go to the gym: what routine should we do? Do you want to define or create a volume? Do you need to change your routine?</p>
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<p>What should you do if you are new to the gym?</p>
<p>The short and quick answer is that if you are new to the gym, the first training program you should do is a basic conditioning program to work and prepare muscles and joints. This general conditioning training program should be basic to the whole body and last 10-12 weeks. After this, you need to weigh the different training programs out there and decide which direction to take.</p>
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<p>&nbsp;</p>
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" alt="Published on Unplash by Alora Griffiths"></p>
<p>Published on Unplash by Alora Griffiths</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<h3>strength routine</h3>
<p>&nbsp;</p>
<p>The strength routine is based on moving heavy weights and performing several iterations for each set. As a rule, we will work with a load of 75% to 90% of 1 RM (maximum repetition), so we will describe a series made up of 3 to 6 repetitions.</p>
<p>&nbsp;</p>
<h3>hypertrophy routine</h3>
<p>&nbsp;</p>
<p>The hypertrophy routine is a routine used to increase muscle mass and consists of 6-12 repetitions, so the weight used is significantly higher than that used in the strength routine. In this case, the load will be in the range of 60-70% of 1RM.</p>
<p>&nbsp;</p>
<h3>definition routine</h3>
<p>&nbsp;</p>
<p>In general, the definition routine has more repetitions than a normal routine. In this case, series of 12 to 15 repetitions, even reaching 18 repetitions per series.</p>
<p>&nbsp;</p>
<p>As these are series of high repetitions, we must proportionally reduce the weight moved, so in this case the loads will oscillate between 40-60% of our 1RM.</p>
<p>&nbsp;</p>
<h3>Volume or definition?</h3>
<p>&nbsp;</p>
<p>If you&#8217;re new to the gym, the usual answer is none. If you&#8217;re starting your gym workout for the first time, or if you&#8217;re starting to train after an extended period of time, it&#8217;s a good idea to get into a general conditioning routine. This is usually an adaptation period of at least 8 weeks (2 months). After this period, it&#8217;s time to check your form and future goals, so ask yourself when you get there.</p>
<p>&nbsp;</p>
<p><img 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" alt="Published on Unplash by Krists Šidlovskis"></p>
<p>Published on Unplash by Krists Šidlovskis</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p>At the end of this adaptation period, which is mandatory for beginners, unless you are overweight or obese where you need to continue losing weight, start with a volume phase to gain muscle mass. Otherwise you can destroy muscle mass in the definition phase and not achieve optimal results.</p>
<p>&nbsp;</p>
<p>However, it should be noted that the diet is more important than the routine to follow. Without a proper diet, it is impossible for our routine to work. In addition, energy is lost if it does not receive the adequate quantity and quality of nutrients, we will not achieve our objectives either.</p>
<p>&nbsp;</p>
<h3>Volume phase: routine + diet</h3>
<p>&nbsp;</p>
<p>As we have previously commented, for a volume phase, our routine will be made up of series of between six and twelve repetitions (around 60-75% of our 1RM the weight used).</p>
<p>&nbsp;</p>
<p>For the volume phase to work, it is necessary to adjust the food and follow a high-calorie diet. Care should be taken not to increase calories too much, especially in those cases that accumulate excess fat. Therefore, it is recommended that the increase in the amount of calories consumed by our diet be 10-15%.</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p><img 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" alt="Published on Unplash by Joe Ciciarelli"></p>
<p>Published on Unplash by Joe Ciciarelli</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<h3>Definition phase: routine + diet</h3>
<p>&nbsp;</p>
<p>You have now finished the volume phase and want to run the definition phase. How can you do it correctly?</p>
<p>&nbsp;</p>
<p>The first step is to adjust your diet. As with the bulking phase, caloric intake must be coordinated with training. In this case, the same, but in reverse, that is, a hypocaloric diet. To do this, you need to reduce your calorie intake by no more than 10-15%. Otherwise, you will lose a significant amount of energy and your routine will not work properly.</p>
<p>&nbsp;</p>
<p>And for the routines, as we&#8217;ve seen before, these are routines that have a higher number of repetitions than volume routines. For this type of routine, the series consists of 12 to 15 repetitions. The definition routine uses 40-60% by weight of 1RM.</p>
<p>&nbsp;</p>
<h3>Do you need to change your training routine frequently?</h3>
<p>&nbsp;</p>
<p>This is a question whose answer is a bit complicated. First of all you tell yourself that you don&#8217;t have to change your routine as usual, because if you do, your body never adapts to the stimulus of doing the routine.</p>
<p>&nbsp;</p>
<p>When you start a routine, your body receives external stimuli and tries to adapt to them. This phase of adaptation to the routine usually lasts from 6 to 12 weeks (that is, from a month and a half to three months).</p>
<p>&nbsp;</p>
<p>As with most people, if we make routine changes every eight weeks or less, we don&#8217;t have enough time for our bodies to adjust to the stimuli they receive from our daily lives. Therefore, it is likely that if we change the routine before 8 weeks, the routine will not have an effect. However, that doesn&#8217;t mean you always have to change your routine after that time.</p>
<p>&nbsp;</p>
<p>From time to time, simply changing training sessions can give amazing results: higher reps and reduced sets, changes in training intensity, changes in running angle or grip type for a particular exercise… As you experiment, You don&#8217;t have to change the whole routine.</p>
<p>&nbsp;</p>
]]></content:encoded>
					
		
		
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