Training Volume Increases Or Maintenance Based On Previous Volume: The Effects On Muscular Adaptations In Trained Males

Table of Contents

Overall Summary

Study Background and Main Findings

This study investigated the effects of increasing resistance training volume on muscle hypertrophy and strength in trained males. All groups showed significant increases in regional fat-free mass (ROI-FFM) and muscle thickness (MT), with no significant differences between groups. The control group (CON) surprisingly demonstrated the highest overall 1RM back squat strength gains (p < 0.05), while the group with a 30% volume increase (G30) showed the greatest improvement in repetitions to failure (RTF). These findings suggest that maintaining a moderate training volume can be sufficient for muscle growth and may be superior for maximal strength gains in trained individuals, while a 30% increase in volume might benefit strength-endurance.

Research Impact and Future Directions

The study provides valuable insights into the effects of individualized resistance training volume increases on muscle hypertrophy and strength adaptations in trained males. While the results indicate that all groups experienced similar muscle growth, the control group demonstrated superior maximal strength gains, and the 30% increase group showed improved strength-endurance. These findings suggest a complex relationship between training volume and adaptations, highlighting the potential limitations of simply increasing volume for trained individuals and emphasizing the importance of considering individual responses and training goals.

The study's practical utility is somewhat limited by its specific focus on resistance-trained males and the relatively short intervention period. However, the findings challenge the conventional wisdom that "more is always better" when it comes to training volume, suggesting that a moderate volume may be sufficient for muscle growth and even optimal for strength gains in this population. These results are placed within the context of existing literature, although a more thorough comparison with studies on individualized volume increases would further strengthen the discussion.

Practitioners can use these findings to inform their training recommendations, particularly for experienced lifters. Maintaining a moderate training volume (around 12 weekly sets per muscle group) appears to be a viable strategy for promoting muscle growth and strength gains. While a 30% increase in volume may enhance strength-endurance, further increases may not provide additional benefits and could even hinder strength development. However, it is crucial to acknowledge the study's limitations, including the specific population studied, the short-term nature of the intervention, the lack of nutritional control, and the high dropout rate. These factors may limit the generalizability of the findings and warrant caution in their interpretation.

Several critical questions remain unanswered. The study's design does not allow for a definitive determination of the optimal training volume for different populations or training goals. Additionally, the mechanisms underlying the unexpected strength gains in the control group are not fully understood. While the methodological limitations, particularly the high dropout rate and lack of nutritional control, do not fundamentally invalidate the conclusions, they do highlight the need for further research with larger sample sizes, longer intervention periods, and more diverse populations. Future studies should also investigate the long-term effects of individualized volume increases and explore the potential influence of other training variables.

Critical Analysis and Recommendations

Clear study design (written-content)
The methods section clearly defines the study's design as a parallel-group repeated-measures design with counterbalanced randomization. This enhances the internal validity of the study by controlling for potential confounding variables and ensuring that the groups are comparable at baseline.
Section: Material & Methods
Specific participant criteria (written-content)
The inclusion and exclusion criteria for participants are well-defined, ensuring the selection of a homogenous sample of resistance-trained males. This strengthens the study's internal validity and allows for more precise conclusions about the effects of the intervention on the target population.
Section: Material & Methods
Detailed data collection procedures (written-content)
The procedures for data collection are described in detail, including specific equipment, measurement protocols, and timing of assessments. This level of detail strengthens the study's reproducibility, allowing other researchers to replicate the methods and verify the findings.
Section: Material & Methods
Justify initial sample size target (written-content)
The section mentions a post-hoc power analysis but lacks a clear justification for the initial recruitment target. Adding a rationale for the initial sample size target, even if it was not based on a formal a priori power analysis, would strengthen the paper by demonstrating a thoughtful approach to study design and increasing confidence in the reported findings.
Section: Material & Methods
Report effect sizes for all outcomes (written-content)
The section reports p-values and confidence intervals but lacks effect sizes, which are crucial for understanding the magnitude of the observed effects. Adding effect sizes would strengthen the paper by providing a more complete picture of the intervention's impact and facilitating meta-analyses.
Section: Results
Expand analysis of unexpected strength findings (written-content)
The discussion acknowledges the unexpected superior performance of the CON group in 1RM but lacks a thorough exploration of potential underlying mechanisms. Expanding on this analysis would strengthen the paper by demonstrating a critical evaluation of the results and offering potential explanations for the observed discrepancies.
Section: Discussion
Analyze impact of high dropout rate (written-content)
The section mentions the dropout rate but does not fully explore how this attrition might have affected the study's findings and conclusions. Expanding on this analysis would strengthen the paper by demonstrating a critical evaluation of the study's limitations and their potential influence on the results.
Section: Limitations
Integrate study limitations into interpretation (written-content)
The conclusion summarizes the results but doesn't fully integrate the limitations discussed in the preceding section. Integrating the limitations into the conclusion would strengthen the paper by demonstrating a critical awareness of the study's constraints and their potential impact on the interpretation of the results.
Section: Conclusions

Section Analysis

Abstract

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Key Aspects

Strengths

Suggestions for Improvement

Material & Methods

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Figure 1. CONSORT flow diagram
First Reference in Text
A CONSORT flow diagram of the study is presented in Figure 1.
Description
  • Purpose of a CONSORT Flow Diagram: A CONSORT flow diagram is a visual tool used in scientific research, particularly in clinical trials, to show the flow of participants through a study. CONSORT stands for Consolidated Standards of Reporting Trials. Think of it like a map that tracks the journey of each participant from the beginning to the end of the research.
  • Stages of Participant Flow: The diagram typically starts with the total number of individuals assessed for eligibility. It then shows how this initial group is narrowed down based on specific criteria, like a funnel. Some people might not meet the study's requirements (exclusion criteria), and this is clearly indicated. The remaining participants are then randomly assigned (randomization) into different groups, such as treatment and control groups.
  • Tracking Participant Progress: As the study progresses, the diagram tracks what happens to each group. It shows how many participants complete each phase of the study and how many drop out, along with the reasons for their withdrawal. This part is crucial for understanding the study's retention rate and potential biases that could arise if many participants leave.
  • Final Analysis: Finally, the diagram indicates the number of participants included in the final analysis for each group. This helps readers see how the data was analyzed and whether the analysis included all participants or only those who completed the study. In this specific diagram, it indicates that there are different types of analyses done, specifically Mixed Model analysis and ANCOVA (Analysis of Covariance) analysis. Mixed Model analysis is a statistical method that handles missing data points. ANCOVA analysis is a method that assesses the differences between groups while controlling for other variables that might influence the outcome.
Scientific Validity
  • Adherence to CONSORT Guidelines: The CONSORT flow diagram appears to adhere to the standard CONSORT guidelines, providing a clear and transparent account of participant progression throughout the study. The inclusion of specific numbers at each stage (enrollment, allocation, follow-up, and analysis) is consistent with best practices for reporting clinical trials and enhances the study's reproducibility.
  • Detailed Accounting of Participant Attrition: The diagram provides a detailed account of participant attrition, including reasons for dropouts. This level of detail is crucial for assessing potential biases. For instance, a high dropout rate in one group or for a specific reason (e.g., injury related to the study) could impact the study's internal validity. The diagram allows readers to evaluate these factors effectively.
  • Transparency in Group Allocation and Analysis: The diagram clearly shows the number of participants allocated to each group (CON, G30, G60) and specifies the number of participants included in each type of analysis (Mixed Model and ANCOVA). This transparency is important for understanding the statistical power of each analysis and for assessing the generalizability of the findings.
Communication
  • Clarity and Readability: The flow diagram is well-organized and easy to follow. The use of distinct boxes and arrows clearly delineates the flow of participants through the study. The color-coding of different stages (e.g., Enrollment in blue) further enhances readability. It effectively communicates the complex process of participant flow in a concise and understandable manner.
  • Use of Text Labels: The text labels within each box are concise and informative. They provide sufficient detail to understand each stage without being overly verbose. However, the label "COVID-19 restrictions" could benefit from a slightly more detailed explanation, as the specific nature of these restrictions is not immediately clear.
  • Visual Appeal and Simplicity: The diagram maintains a simple and uncluttered design, which aids in its visual appeal and ease of interpretation. The use of a single color scheme for each group throughout the diagram is consistent and helps to visually distinguish the groups. Overall, the diagram is effective in conveying key information about the study's methodology in a visually engaging manner.
Figure 2. Overview of the region-of-interest fat-free mass (ROI-FFM) and...
Full Caption

Figure 2. Overview of the region-of-interest fat-free mass (ROI-FFM) and anterior thigh muscle thickness: RF- Rectus femoris, VI- Vastus intermedius. A- ROI-FFM, B- proximal muscle thickness, and (C) distal muscle thickness assessments.

First Reference in Text
The lower body was subdivided from the iliac crest to the lateral condyle for each scan at pre-testing and post-testing (Figure 2A).
Description
  • Overview of Presented Images: This figure shows how the researchers measured body composition, specifically fat-free mass and muscle thickness in the thigh. It's like taking a detailed look inside the body to see what's under the skin, focusing on muscle and excluding fat. The figure is divided into three parts (A, B, and C) to illustrate different measurement techniques.
  • Explanation of Image 2A: Image 2A shows an X-ray-like picture, which is a scan of the lower body. This is a visual representation of a technique called dual-energy X-ray absorptiometry (DEXA), which is like a sophisticated scale that can differentiate between bone, fat, and lean tissue (muscle). The 'region-of-interest fat-free mass' (ROI-FFM) refers to the amount of muscle in a specific area, in this case, from the top of the hip bone (iliac crest) down to the outer side of the knee (lateral condyle). Imagine drawing a box on the body from the hip to the knee and measuring only the muscle within that box, excluding everything else.
  • Explanation of Images 2B and 2C: Images 2B and 2C are ultrasound images showing the thickness of the thigh muscles. Ultrasound is like using sound waves to 'see' inside the body, similar to how bats use echolocation. Image B shows the 'proximal muscle thickness,' which is the muscle thickness closer to the hip, while Image C shows the 'distal muscle thickness,' which is the muscle thickness closer to the knee. The labels 'RF' and 'VI' point out two specific muscles: the Rectus Femoris, which is the large muscle at the front of the thigh that helps extend the knee, and the Vastus Intermedius, which lies beneath the Rectus Femoris and also assists in knee extension.
Scientific Validity
  • Appropriateness of Measurement Techniques: The use of DEXA for assessing ROI-FFM and ultrasound for measuring muscle thickness are both well-established and valid methods in exercise science. DEXA is considered a gold standard for body composition analysis, and ultrasound provides a reliable, non-invasive way to measure muscle dimensions. The combination of these techniques provides a comprehensive assessment of both overall lean mass and specific muscle size changes.
  • Specificity of Anatomical Landmarks: The description clearly defines the anatomical landmarks used for the measurements (iliac crest to lateral condyle for DEXA, and proximal and distal portions of the thigh for ultrasound). This specificity is crucial for ensuring consistency and reproducibility of the measurements. The reference to established anatomical landmarks enhances the scientific rigor of the study.
  • Relevance to Study Objectives: The measurements depicted in Figure 2 are directly relevant to the study's objectives of assessing muscle hypertrophy and body composition changes. By visualizing these specific measurements, the figure helps to establish the link between the intervention (resistance training) and the measured outcomes (changes in muscle size and fat-free mass).
Communication
  • Clarity of Image Labeling: The images are clearly labeled, with distinct panels (A, B, C) and clear indications of the anatomical structures being measured (RF, VI, Femur). The use of arrows and labels within the images helps to guide the reader's attention to the relevant features. However, the specific measurement lines in images B and C could be more prominent to clearly show where the thickness is being measured.
  • Effectiveness of Caption: The caption effectively describes the content of the figure, providing a brief overview of each panel. The abbreviations (ROI-FFM, RF, VI) are defined, which is essential for readers unfamiliar with these terms. The caption could be slightly improved by explicitly stating that image A represents a DEXA scan, as this is not immediately obvious from the image alone.
  • Visual Quality of Images: The quality of the images is generally good, particularly the ultrasound images (B and C), which provide clear visualization of the muscle tissue. The DEXA scan image (A) is less visually striking but still adequate for illustrating the region of interest. The contrast in the ultrasound images could be slightly enhanced to better differentiate between the muscle and surrounding tissues.
Table 1. Characteristics of all subjects who completed baseline testing.
First Reference in Text
Data from 55 participants, including 29 of whom completed the study, were used for the statistical analysis (Table 1).
Description
  • Purpose of the Table: This table summarizes the characteristics of the people who participated in the study, specifically those who completed the initial testing. Think of it like a snapshot of the group's starting conditions before the experiment began. It provides information about the participants in each of the three groups: CON, G30, and G60.
  • Information Presented: The table lists several characteristics for each group, including the number of participants (N), average body mass (in kilograms), average age (in years), average body fat percentage (BF%), average previous set number per week (PSN), average weekly set number performed during the study (SPS), the average number of sets added to PSN for the intervention protocol (SET_DIFF), and the average squat 1RM:BM (a ratio of the maximum weight they could squat for one repetition to their body mass). These are all factors that could potentially influence the results of the study.
  • Explanation of Terms: BF% is a measure of how much of a person's body weight is made up of fat. PSN (Previous Set Number) is the number of sets of exercises they were doing each week before the study. SPS (Study Set Number) is the number of sets they did each week during the study. SET_DIFF is the difference between the number of sets they did before the study and the number they did during the study. Squat 1RM:BM is a measure of their strength relative to their body weight. For example, if someone can squat 100 kg and they weigh 50 kg, their Squat 1RM:BM would be 2.0.
Scientific Validity
  • Relevance to Study Design: Presenting baseline characteristics is crucial for demonstrating the initial equivalence (or lack thereof) of the experimental groups. This is important for establishing the internal validity of the study, as any pre-existing differences between groups could confound the results. By comparing the baseline characteristics, readers can assess whether any observed effects are likely due to the intervention or to pre-existing differences between the groups.
  • Completeness of Data: The table appears to present data for all participants who completed baseline testing (N=55), which is consistent with the reference text. However, it's worth noting that the reference text also mentions that only 29 participants completed the entire study. This discrepancy in the number of participants who completed baseline testing versus the entire study should be clarified and its implications for the generalizability of the findings should be discussed.
  • Appropriateness of Variables: The variables included in the table are relevant to the study's aims and are commonly reported in exercise science research. They provide a comprehensive overview of the participants' physical characteristics and training history. However, it might be beneficial to include additional variables, such as training experience (e.g., years of resistance training), to further characterize the sample.
  • Statistical Analysis: While the table itself does not present statistical analyses, it is implied that these characteristics were compared between groups to assess baseline equivalence. The methods section should clearly describe the statistical tests used for these comparisons (e.g., ANOVA, t-tests). Reporting the p-values for these comparisons in the table would further enhance its scientific value.
Communication
  • Clarity of Column Headers: The column headers are clear and concise, providing a brief description of each variable. The use of abbreviations (e.g., BF%, PSN, SPS) is appropriate given the space constraints of a table, and these abbreviations are defined in the footnote. However, the header "SET_DIFF (Sets.week¹)" could be improved by providing a more descriptive label, such as "Change in Weekly Sets."
  • Organization and Readability: The table is well-organized and easy to read. The use of distinct rows for each variable and columns for each group allows for a clear comparison of characteristics. The table is not overly cluttered, and the information is presented in a logical order. The use of bold font for the group names (CON, G30, G60) enhances readability.
  • Footnote Clarity: The footnote provides clear definitions for the abbreviations used in the table. However, the definition of SET_DIFF could be made more explicit by stating that it represents the difference between SPS and PSN. Additionally, the footnote could be improved by briefly explaining the relevance of each variable to the study's aims.
  • Overall Effectiveness: The table effectively communicates the baseline characteristics of the participants in each group. It provides a concise summary of the key variables that could potentially influence the study's results. The table is well-designed and easy to understand, making it an effective tool for conveying this important information.

Results

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Figure 3. The average weekly volume load for the intervention exercises...
Full Caption

Figure 3. The average weekly volume load for the intervention exercises throughout 16 sessions across experimental groups. Control group (CON – blue line), 30% increase in the number of sets (G30 - orange line), and 60% increase in the number of sets (G60 – green line).

First Reference in Text
The average weekly volume load throughout 16 sessions for the experimental groups is presented in Figure 3.
Description
  • What the Graph Represents: This graph shows the average weekly "volume load" for three different groups of people doing exercises over 16 sessions. Imagine you're lifting weights; the volume load is a way to measure how much total work you've done. It's calculated by multiplying the weight you lift by the number of times you lift it (repetitions) and the number of sets you do. So, if you lift 10 kg for 3 sets of 10 repetitions each, your volume load for that exercise would be 10 kg x 3 sets x 10 reps = 300 kg. This graph tracks this total work done each week for each group.
  • Description of the Groups: There are three groups represented by different colored lines: blue for the control group (CON), orange for the group with a 30% increase in the number of sets (G30), and green for the group with a 60% increase in the number of sets (G60). The control group is like a baseline; they're doing their usual routine. The other two groups are doing more sets than they normally would, with G60 doing the most extra work. 'Sets' here refers to the number of times a group of repetitions is performed. For example, doing 10 push-ups, resting, and then doing another 10 push-ups means you did 2 sets of push-ups.
  • How the Data is Displayed: The graph has 'Volume Load (kg)' on the vertical axis (the up-and-down line) and 'Session' on the horizontal axis (the side-to-side line). Each point on the lines represents the average volume load for that group at that particular session. The shaded areas around the lines are likely showing the variability or spread of the data, like a range of how much volume load different people in the same group did. This helps us see not just the average, but also how much individual results differed within each group.
Scientific Validity
  • Relevance to Study's Aim: This figure directly addresses the study's aim of investigating the effects of different training volumes. By plotting the average weekly volume load, it provides a clear comparison of the training stimulus experienced by each group. This is crucial for understanding the relationship between training volume and the observed outcomes (muscle growth, strength, etc.). The validity of using volume load as a measure of training stimulus is well-established in the field of exercise science.
  • Accuracy of Representation: The figure accurately represents the intended data, as described in the caption and methods. The use of separate lines for each group allows for a direct visual comparison of their training volumes. The inclusion of error bars or shaded areas to represent variability (e.g., standard deviation or confidence intervals) enhances the scientific rigor by providing information about the precision of the estimates.
  • Limitations in Generalizability: While the figure provides valuable information about the volume load within the study, it's important to note that these values are specific to the exercises and protocols used. Generalizing these absolute volume load numbers to other exercises or training programs should be done with caution, as different exercises and populations may have different volume-load responses.
Communication
  • Clarity of Group Differentiation: The use of distinct colors (blue, orange, green) for each group makes it easy to distinguish between them. The legend clearly identifies which color corresponds to each group (CON, G30, G60), enhancing the readability of the graph. The labels are concise and informative, accurately describing the experimental manipulation (e.g., "30% increase in the number of sets").
  • Effectiveness of Axis Labels: The axis labels are clear and informative. "Volume Load (kg)" on the y-axis provides a precise definition of the measured variable, including the unit of measurement. "Session" on the x-axis clearly indicates the time points at which the measurements were taken. The use of a consistent scale on the y-axis across all groups allows for a direct visual comparison of volume load between groups.
  • Use of Shaded Areas: The shaded areas around the lines, likely representing the standard deviation or confidence intervals, are visually effective in conveying the variability within each group. However, the exact meaning of the shaded areas should be explicitly stated in the caption or figure legend to avoid ambiguity. The overlapping shaded areas also provide a visual indication of the degree of overlap between groups, which is helpful in interpreting the statistical significance of any observed differences.
  • Overall Visual Appeal: The graph is visually appealing and easy to understand. The use of a simple line graph format, distinct colors, and clear labels makes it effective in communicating the key findings. The graph is not overly cluttered, and the information is presented in a logical and intuitive manner. The overall design of the graph contributes to its effectiveness in conveying the intended message.
Figure 4. Delta-change in fat-free mass and muscle thickness per participant....
Full Caption

Figure 4. Delta-change in fat-free mass and muscle thickness per participant. Scatterplot of individual changes in muscle thickness plotted against the change in region-of-interest fat-free mass. The size of the marker indicates the subject's volume load. Density plots for the responses are plotted on respective axis spines.

First Reference in Text
Individual responses for each experimental condition (CON, G30, G60) are visualized using a bubble scatter plot (Figure 4).
Description
  • What the Graph Shows: This graph is a scatterplot, which means it uses dots to represent data points. Each dot shows the change in two measurements for a single participant: how much their muscle thickness changed and how much their fat-free mass in a specific region (region-of-interest fat-free mass) changed. Think of it like plotting each person's muscle growth against their lean mass gains on a graph. 'Delta-change' simply means the amount of change from the beginning to the end of the study.
  • Explanation of the Axes: The horizontal axis (the side-to-side line) represents the change in region-of-interest fat-free mass (measured in grams), which is essentially the change in lean mass in a specific area of the body. The vertical axis (the up-and-down line) represents the change in muscle thickness (measured in centimeters). So, each dot's position tells you how much a person's lean mass and muscle thickness changed.
  • Meaning of the Marker Size: The size of each dot (or 'marker') represents the participant's volume load. As explained before, volume load is a measure of the total work done during exercise (weight x reps x sets). A bigger dot means a higher volume load, so participants who lifted more weight, did more repetitions, or completed more sets have larger dots. This allows us to see if there's a relationship between the amount of work done and the changes in muscle thickness and lean mass.
  • Explanation of Density Plots: The shaded areas along the edges of the graph are called density plots. They show the distribution of the data for each axis. Imagine you're looking at a mountain range from the side; the density plot is like a silhouette of that mountain, showing where most of the data points are clustered. A taller peak in the density plot means that many participants had similar changes in that measurement. This helps us see the overall trends in the data, like whether most people gained a lot of muscle thickness or just a little.
Scientific Validity
  • Visualization of Individual Variability: This figure is valuable because it shows individual data points rather than just group averages. This allows for the assessment of individual variability in response to the training interventions. By plotting each participant's change in muscle thickness against their change in fat-free mass, the figure helps to reveal potential relationships between these variables and how they might be influenced by training volume (indicated by marker size).
  • Appropriateness of Using a Scatterplot: A scatterplot is an appropriate choice for visualizing the relationship between two continuous variables, as is the case here. The addition of marker size to represent a third variable (volume load) is a useful way to incorporate additional information into the plot. However, the scientific validity of this approach depends on the assumption that volume load is linearly or monotonically related to the marker size, which should be clearly stated.
  • Use of Density Plots: The inclusion of density plots along the axes enhances the figure's scientific value by providing information about the distribution of each variable. This allows for a more nuanced interpretation of the data, beyond simply looking at the scatterplot alone. For example, the density plots can reveal whether the data are normally distributed, skewed, or bimodal, which can have implications for the statistical analyses and interpretation of the results. However, it is important to note that the shape of the density plot is affected by the choice of bandwidth. The chosen bandwidth should be justified in the methods or figure caption.
Communication
  • Clarity of the Caption: The caption is generally clear and informative, providing a concise description of the figure's content. It explains the meaning of the axes, the marker size, and the density plots. However, it could be improved by explicitly stating the units of measurement for each axis (grams for fat-free mass and centimeters for muscle thickness) and by providing a brief interpretation of the expected relationship between the variables.
  • Effectiveness of Color and Marker Size: The use of different colors to represent the three experimental groups (CON, G30, G60) is effective in visually distinguishing the groups. However, the color scheme could be improved by using more contrasting colors to enhance readability. The use of marker size to represent volume load is visually intuitive, with larger markers indicating higher volume loads. However, the exact relationship between volume load and marker size should be specified in the caption (e.g., linear, logarithmic) to aid in interpretation.
  • Density Plot Presentation: The density plots are well-integrated into the figure and provide valuable information about the distribution of the data. However, they could be made more prominent by using a darker shade or a thicker line. Additionally, the y-axis label for the density plot ("Density") is missing, which could cause confusion for some readers. It is also worth considering whether separate density plots for each experimental group would enhance the figure's interpretability, especially if the groups exhibit distinct distributions.
  • Overall Visual Appeal: The scatterplot is generally well-designed and visually appealing. The use of a clean, uncluttered layout, clear axis labels, and a legend for the experimental groups makes the figure easy to understand. The inclusion of a diagonal line representing the overall trend (r = 0.71) is helpful in visualizing the relationship between the two main variables. However, the figure could be further improved by adding gridlines to facilitate the reading of specific data points and by increasing the font size of the axis labels and legend to enhance readability.
Figure 5. Delta change in repetition to failure and 1RM per participant....
Full Caption

Figure 5. Delta change in repetition to failure and 1RM per participant. Scatterplot of individual changes in one-repetition-maximum plotted against the change in repetitions-to-failure. The size of the marker indicates the subject's volume load. Density plots for the responses are plotted on respective axis spines.

First Reference in Text
Individual responses for each experimental condition (CON, G30, G60) are visualized using a bubble scatter plot in Figure 5.
Description
  • What the Graph Shows: This graph is a scatterplot, similar to Figure 4. Each dot represents one participant in the study. The position of each dot shows how much that participant's one-repetition maximum (1RM) and their repetitions-to-failure changed over the course of the study. '1RM' stands for one-repetition maximum, which is the heaviest weight a person can lift for one repetition of an exercise. 'Repetitions-to-failure' means the number of times someone can do an exercise before they can't do it anymore. 'Delta change' means the amount of change from the beginning to the end of the study. In this graph, we are looking at changes in strength.
  • Explanation of the Axes: The horizontal axis (side-to-side) shows the change in repetitions-to-failure. The vertical axis (up-and-down) shows the change in 1RM, measured in kilograms (kg). So, if a dot is high up on the graph, it means that the participant's 1RM increased a lot. If a dot is far to the right, it means that the participant's repetitions-to-failure increased a lot.
  • Meaning of the Marker Size: Just like in Figure 4, the size of each dot represents the participant's volume load. A bigger dot means the participant did more total work during the study (weight lifted x number of repetitions x number of sets). This helps us see if there's a connection between how much work someone did and how much their strength improved.
  • Explanation of Density Plots: The shaded areas along the edges of the graph are density plots. They show where most of the data points are clustered for each measurement (change in 1RM and change in repetitions-to-failure). A taller peak means that many participants had similar changes in that measurement. These density plots help us see the overall trends in the data, like whether most people improved their 1RM a lot or just a little.
Scientific Validity
  • Visualization of Strength Changes: This figure effectively visualizes the individual changes in two key strength-related variables: 1RM and repetitions-to-failure. By plotting these variables against each other, the figure allows for the assessment of potential relationships between them and how they might be influenced by training volume (indicated by marker size). This is relevant to the study's aim of investigating the effects of different training volumes on strength adaptations.
  • Appropriateness of Using a Scatterplot: Similar to Figure 4, a scatterplot is an appropriate choice for visualizing the relationship between two continuous variables. The addition of marker size to represent volume load is a useful way to incorporate additional information into the plot. However, the scientific validity of this approach depends on the assumption that volume load is linearly or monotonically related to the marker size, which should be clearly stated and justified.
  • Use of Density Plots: The inclusion of density plots along the axes enhances the figure's scientific value by providing information about the distribution of each variable. This allows for a more nuanced interpretation of the data. For instance, the density plots can reveal whether the data are normally distributed, skewed, or bimodal, which can have implications for the statistical analyses and interpretation of the results. The shape of the density plot is affected by the choice of bandwidth, thus the chosen bandwidth should be justified in the methods or figure caption.
Communication
  • Clarity of the Caption: The caption is generally clear and informative, providing a concise description of the figure's content. It explains the meaning of the axes, the marker size, and the density plots. However, it could be improved by explicitly stating the units of measurement for each axis (kg for 1RM and number of repetitions for repetitions-to-failure) and by providing a brief interpretation of the expected relationship between the variables.
  • Effectiveness of Color and Marker Size: The use of different colors to represent the three experimental groups (CON, G30, G60) is effective in visually distinguishing the groups. However, the color scheme could be improved by using more contrasting colors to enhance readability. The use of marker size to represent volume load is visually intuitive, with larger markers indicating higher volume loads. The exact relationship between volume load and marker size should be specified in the caption (e.g., linear, logarithmic) to aid in interpretation.
  • Density Plot Presentation: The density plots are well-integrated into the figure and provide valuable information about the distribution of the data. They could be made more prominent by using a darker shade or a thicker line. The y-axis label for the density plot is missing, potentially causing confusion. It is also worth considering whether separate density plots for each experimental group would enhance the figure's interpretability, especially if the groups exhibit distinct distributions.
  • Overall Visual Appeal: The scatterplot is generally well-designed and visually appealing. The use of a clean, uncluttered layout, clear axis labels, and a legend for the experimental groups makes the figure easy to understand. The inclusion of a diagonal line representing the overall trend (r = -0.20) is helpful in visualizing the relationship between the two main variables. The figure could be further improved by adding gridlines to facilitate the reading of specific data points and by increasing the font size of the axis labels and legend to enhance readability.
Table 2. Region-of-interest fat-free mass and muscle thickness assessments...
Full Caption

Table 2. Region-of-interest fat-free mass and muscle thickness assessments (mean ± SD)

First Reference in Text
Pairwise between groups; CON vs G30: 0.90kg [95%-CI: 0.63 to 1.18 kg, p=0.32]; CON vs G60: 0.97kg [95%-CI: 0.71 to 1.24 kg, p=0.23], and G30 vs G60: 0.07kg [95%-CI: -0.21 to 0.35 kg, p=0.94].
Description
  • Purpose of the Table: This table presents the results of the measurements of fat-free mass and muscle thickness in specific regions of the body. It compares these measurements between the three groups (CON, G30, and G60) both before (Pre) and after (Post) the intervention. Think of it like showing the 'before' and 'after' pictures of the body composition changes for each group.
  • Measurements Presented: The table shows the average (mean) and the spread of the data (standard deviation, indicated by ± SD) for each measurement. The measurements include region-of-interest fat-free mass (ROI-FFM), which is the amount of lean mass in a specific area, and muscle thickness at two locations: proximal (PMT) and distal (DMT). It also includes the sum of proximal and distal muscle thickness (ΣΜΤ). Imagine measuring the amount of muscle in your arm, the thickness of the muscle near your shoulder (proximal) and near your elbow (distal), and then adding those two thicknesses together.
  • Structure of the Table: The table is organized with the different measurements (ROI-FFM, PMT, DMT, ΣΜΤ) listed in rows and the three groups (CON, G30, G60) listed in columns. For each group, it shows the average measurement before the intervention (Pre) and after the intervention (Post), along with the number of participants (n) in each group for each measurement. This allows us to see how each group changed from the beginning to the end of the study.
  • Explanation of 95% CI and ES: 95% CI refers to the 95% confidence interval. This is a range of values that we are 95% confident contains the true average value for the population. It helps to understand how precise the sample average is. For example, a 95% CI of 0.63 to 1.18 kg means that we are 95% confident that the true average difference between CON vs G30 is between 0.63 kg and 1.18 kg. ES refers to effect size. Effect size is a way to measure the strength of the relationship between two variables. In this table, it is used to quantify the magnitude of the change in each measurement from pre- to post-test.
Scientific Validity
  • Relevance to Study Aims: This table directly addresses the study's primary aims of assessing changes in body composition (fat-free mass and muscle thickness) in response to different training volumes. By presenting the pre- and post-intervention measurements for each group, the table allows for a direct comparison of the effects of the different interventions (CON, G30, G60) on these key outcome variables.
  • Appropriateness of Statistical Descriptors: The use of mean ± SD is appropriate for describing the central tendency and variability of the data. The inclusion of the number of participants (n) for each measurement is important for assessing the sample size and potential for statistical power. The inclusion of 95% confidence intervals (CI) and effect sizes (ES) further enhances the scientific rigor by providing information about the precision and magnitude of the observed effects. However, the table only reports the main time effect and does not present the p-values for the group comparisons or the group-by-time interactions. Including these p-values would provide a more complete picture of the statistical significance of the observed changes.
  • Consistency with Reference Text: The reference text provides specific pairwise comparisons for ROI-FFM, which are not included in the table. While the table presents the necessary data to calculate these comparisons, it would be more informative to include the pairwise comparisons (and their corresponding p-values) directly in the table or in a separate table. This would facilitate a more direct assessment of the differences between groups.
  • Clarity of Variable Definitions: The variables (ROI-FFM, PMT, DMT, ΣΜΤ) are clearly defined in the footnote, which enhances the interpretability of the table. However, the footnote could be improved by briefly explaining the rationale for choosing these specific measurements and their relevance to the study's aims.
Communication
  • Clarity of Table Layout: The table is well-organized and easy to read. The use of distinct rows for each variable and columns for each group allows for a clear comparison of measurements. The table is not overly cluttered, and the information is presented in a logical order. The use of bold font for the variable names and group names enhances readability.
  • Effectiveness of Column Headers: The column headers are clear and informative, providing a brief description of each group and time point (Pre, Post). The use of abbreviations (CON, G30, G60) is appropriate given the space constraints of a table, and these abbreviations are defined in the caption and throughout the paper. However, the column header "Pre(n)" and "Post(n)" could be improved by providing a more descriptive label, such as "Pre (n)" and "Post (n)" to avoid any potential confusion.
  • Use of Footnote: The footnote provides clear definitions for the abbreviations used in the table, which is essential for readers unfamiliar with these terms. The footnote also explains the asterisk (*) indicating a significant main time effect, which is helpful for interpreting the results. However, as mentioned earlier, the footnote could be improved by briefly explaining the relevance of each variable to the study's aims.
  • Overall Effectiveness: The table effectively communicates the results of the body composition assessments. It provides a concise summary of the key outcome variables and allows for a direct comparison of the changes observed in each group. The table is well-designed and easy to understand, making it an effective tool for conveying this important information. However, the inclusion of p-values for group comparisons and group-by-time interactions would further enhance its informativeness and scientific value.
Table 3. Maximum strength and strength-endurance test assessments (mean ± SD)
First Reference in Text
Our analysis also revealed a main group effect (p<0.0268) in which the CON group had higher 1RM values compared to G30 and G60 (Table 3).
Description
  • Purpose of the Table: This table presents the results of the strength tests, specifically looking at maximum strength (how much weight someone can lift once) and strength-endurance (how many times someone can lift a certain weight before getting tired). It compares these measurements between the three groups (CON, G30, and G60) both before (Pre) and after (Post) the intervention. Think of it like showing the 'before' and 'after' pictures of how strong each group became.
  • Measurements Presented: The table shows the average (mean) and the spread of the data (standard deviation, indicated by ± SD) for each measurement. The measurements include 1RM (one-repetition maximum), which is the maximum amount of weight a person can lift for one repetition of an exercise, and RTF (repetitions to failure), which is the number of times a person can lift a certain weight (in this case, 70% of their 1RM) before they can't lift it anymore. Imagine testing how much weight you can lift just once, and then testing how many times you can lift a lighter weight before you're too tired to lift it again.
  • Structure of the Table: The table is organized with the different measurements (1RM and RTF) listed in rows and the three groups (CON, G30, G60) listed in columns. For each group, it shows the average measurement before the intervention (Pre) and after the intervention (Post), along with the number of participants (n) in each group for each measurement. This allows us to see how each group's strength changed from the beginning to the end of the study.
Scientific Validity
  • Relevance to Study Aims: This table directly addresses the study's aims of assessing changes in strength (both maximum strength and strength-endurance) in response to different training volumes. By presenting the pre- and post-intervention measurements for each group, the table allows for a direct comparison of the effects of the different interventions (CON, G30, G60) on these key outcome variables.
  • Appropriateness of Statistical Descriptors: The use of mean ± SD is appropriate for describing the central tendency and variability of the data. The inclusion of the number of participants (n) for each measurement is important for assessing the sample size and potential for statistical power. The inclusion of 95% confidence intervals (CI) further enhances the scientific rigor by providing information about the precision of the estimates.
  • Incomplete Reporting of Statistical Significance: While the reference text mentions a significant main group effect for 1RM, the table itself does not present the p-values for all the group comparisons or the group-by-time interactions. Including these p-values (for both main effects and interactions) would provide a more complete picture of the statistical significance of the observed changes and allow readers to assess which specific comparisons reached statistical significance. The table only indicates significance from pre-training values and significance between CON and G60 at post-training assessment, but it does not provide the p-values for these comparisons. Additionally, the table reports a significant difference for G30 in RTF from pre- to post-training. However, in the reference text, a significant group-by-time interaction is mentioned, but the table does not report the interaction effect or p-value.
Communication
  • Clarity of Table Layout: The table is well-organized and easy to read. The use of distinct rows for each variable and columns for each group allows for a clear comparison of measurements. The table is not overly cluttered, and the information is presented in a logical order. The use of bold font for the variable names and group names enhances readability.
  • Effectiveness of Column Headers: The column headers are clear and informative, providing a brief description of each group and time point (Pre, Post). The use of abbreviations (CON, G30, G60) is appropriate given the space constraints of a table, and these abbreviations are defined in the caption and throughout the paper. The column header "Pre(n)" and "Post(n)" could be improved by providing a more descriptive label, such as "Pre (n)" and "Post (n)" to avoid any potential confusion.
  • Use of Footnote: The footnote provides clear definitions for the abbreviations used in the table (1RM and RTF), which is essential for readers unfamiliar with these terms. The footnote also explains the asterisk (*) and dagger (†) symbols, indicating significant differences. However, as mentioned earlier, the footnote could be improved by briefly explaining the relevance of each variable to the study's aims and by reporting the actual p-values associated with the significant differences.
  • Overall Effectiveness: The table effectively communicates the results of the strength and strength-endurance assessments. It provides a concise summary of the key outcome variables and allows for a direct comparison of the changes observed in each group. The table is well-designed and easy to understand, making it an effective tool for conveying this important information. However, the inclusion of p-values for all group comparisons (main effects and interactions) would significantly enhance its informativeness and scientific value.

Discussion

Key Aspects

Strengths

Suggestions for Improvement

Limitations

Key Aspects

Strengths

Suggestions for Improvement

Conclusions

Key Aspects

Strengths

Suggestions for Improvement

↑ Back to Top