"If you're holding a center puzzle piece in your hand and staring at the tabletop, it's difficult to determine where to place it. If all of the puzzle is complete except for that one piece, then you know exactly where it goes.”
- Rick Rubin
Introduction
The Dynamic Strength Index (DSI) is a ratio comparing an individual’s ballistic and maximum force production capabilities. Displaying a deficit if you will. Essentially answering the question of how much force can be produced with and without a time constraint. Providing insight to practitioners about the potential programming needs of said individual.
Most commonly the DSI is calculated by dividing an individual’s peak ballistic force by their maximum isometric force produced in two separate assessments (more commonly for the lower- over the upper-body). For example, peak propulsive force from a countermovement jump over peak force achieved from an isometric midthigh pull (1). Where a lower DSI indicates the potential need to improve one’s reactive strength, and a higher the potential need to improve one’s absolute strength. However, by no means does comparing these two task-specific assessments and granular metrics provide a comprehensive view of a physical profile. This article aims to not only cover the basics of the DSI, but explain how to use it as a filter of filters.
Figure 1: Outline of the Dynamic Strength Index, including it's calculation, associated recommendations, and an illustration of force potential verse the DSI ratio.
The basics
Practically, the DSI can be utilized in various ways to optimize the pursuit of force generation with respect to time (I.e., Impulse) or not. Practitioners can use the DSI as a filter for individualizing programming, in athlete monitoring, and in reconditioning scenarios. By regularly assessing an individual’s DSI, they can identify changes in force production allowing for targeted next steps from further assessment to programming interventions.
A high DSI (>0.8) is indicative of one’s ability to exhibit superior performance in dynamic actions due to their ability to generate and transfer force efficiently. As a filter recommendation here would be to prioritize the development of maximal force production while excluding most time constraints. A low DSI (<0.6) tells us the opposite, and with this individual it may be wise to prioritize the expression of force with various time constraints. Lastly, a DSI between 0.6 and 0.8 suggests a concurrent approach is satisfactory (2). Context then calls for the need of additional filters.
Normative data should be longitudinally and continually collected that may alter population-specific guidelines. Key physical performance indicators (KPPIs) of the population need to also be passed through alongside other confounding considerations. Rather than being all-in, this alters the suggested value of the DSI ratio on programming to better suit desired outcomes.
It is important to note that the DSI is just one of many metrics used and it should not be considered in isolation. Combining the DSI with other measures correlated to task specific needs is necessary for a complete 360-degree view. Whether it be correlational data regarding speed and power or targeted to the prevalence of a certain injury, a sport or tactical scenario can not be mimicked outside of it’s arena. Implications of physical characteristics can be weighed, yet aren’t typically solo resultants (3).
Figure 2: A summary of how to use the DSI and mitigate the limitations.
The balancing act: strengths and weaknesses
Understanding the strengths and weaknesses of the DSI should be your first step before implementing it into a system. An obvious strength (pun intended) is that the DSI quantifies the force production capability of an individual dichotomously. Then like other metrics, data can be wrangled to create population norms. Also, the data required to complete the calculation is accessible in many environments and otherwise requires components that can be modified and manipulated (4). All said, the DSI is as simple as simple gets. With ease it can be added to an assessment battery or calculated out from a current protocol.
The caveat is the effectiveness of the strengths relies heavily on the consideration of the DSI’s weaknesses. Addition by subtraction. In a functioning system the additional filters should answer one another’s weaknesses (3). The task specific nature of the inputs need to be understood to overcome the technical limitations of the DSI. Did both inputs improve or decline? Did we see an improvement or decrement in Newtons per kilogram? What is the utility of isometric compared to dynamic assessments?
Further, practitioner interaction with the nuances of the calculation can be a limitation. Personal cumulative performance context and gaps must be understood to maximize the effectiveness of the DSI. A filter for individualization will remain synthetic if it does not strain for holistic impurities. The simplicity of the DSI is attractive yet leaves a breadth of interpretation open to the practitioner.
Figure 3: The filtration process allowing for adequate decision making.
Practical implications
The DSI is continually proven to be a valid and reliable filter for a practitioner to better understand their population’s force producing capabilities (1, 3, 5). It’s a great indicator to further investigate whether an individual could benefit from prioritizing maximal strength, ballistic/reactive strength, or from a concurrent approach (2).
When utilizing the simplicity of the calculation to your advantage it can be extremely useful in creating a cookie trail so to speak. Conversely, if the limitations are not accounted for the DSI can be dubious. This is why the DSI should be used as a secondary measure to an assessment battery correlated to the KPPIs being pursued. The DSI can then be leveraged as a byproduct alongside additional data of primary concern, which then can be used in comparison to normative data considering the technical and tactical elements at play to inform decision making (3).
Alexander J. Morgan, MSc., CSCS, RSCC, CEP
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References
1. Thomas, C, Dos'Santos, T, & Jones, PA. (2017). A comparison of dynamic strength index between team-sport athletes. Sports, (5 (3), 71. A Comparison of Dynamic Strength Index between Team-Sport Athletes - PubMed (nih.gov).
2. Comfort, P, Thomas, C, Dos'Santos, T., Suchomel, TJ., Jones, PA, & McMahon, J. (2018). Changes in dynamic strength index in response to strength training. Sports, 6 (4), 176. Changes in Dynamic Strength Index in Response to Strength Training - PubMed (nih.gov).
3. Suchomel, TJ., Sole, CJ., Bellon, CJ., Stone, MH. (2020). Dynamic strength index: relationships with common performance variables and contextualization of training recommendations. Journal of Human Kinetics, 74, 59-70. Dynamic Strength Index: Relationships with Common Performance Variables and Contextualization of Training Recommendations - PubMed (nih.gov).
4. Haischer, MH., Krzyszkowski, J., Roche, S., & Kipp, K. (2o21). Impulse-based dynamic strength index: considering time-dependent force expression. Journal of Strength and Conditioning Research, 35 (5), 1177-1181. Impulse-Based Dynamic Strength Index: Considering Time-Dependent Force Expression - PubMed (nih.gov).
5. Thomas, C., Jones, PA., & Comfort, P. (2015). Reliability of the dynamic strength index in college athletes. International Journal of Sports Physiology and Performance, 10 (5), 542-545. Reliability of the Dynamic Strength Index in college athletes - PubMed (nih.gov).
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