Case Study: Strength & Conditioning Coach Uses Kinvent to Train Clients Smarter, Push Past Plateaus
Years ago, strength and conditioning coach Ryan Fritz worked at a chiropractor’s office. The chiropractor would take X-rays of new patients but wouldn’t collect additional data. Diagnoses were subjective, and care was based largely on feel.
It’s an approach that Fritz disagreed with – but it’s one that’s pervasive among physical therapists, athletic trainers and coaches. Time and again, Fritz has seen his peers overlook the root cause of a client’s pain or misunderstand what’s holding an athlete back from fulfilling their potential. Why? Because they shun numerical data and rely on guesswork.
Frustrated by the lack of objectivity he encountered in his work, Fritz founded his own science-driven health, fitness and wellness clinic, Science of Cardio, in 2012. Since then, Fritz has leveraged cutting-edge technology, including the Kinvent ecosystem, to gather data that empowers him to look past symptoms, identify the true drivers of pain and performance plateaus, and accelerate improved client outcomes.
The Problem: A Scattershot, Subjective Approach
Over his 20 years in the fitness and rehabilitation industry, Fritz has worked with a range of clients, including amateur athletes preparing for the NFL Scouting Combine and professional baseball players preparing for spring training. Now, he primarily works with older adults, with a focus on longevity and healthy aging.
Throughout his career, Fritz has seen PTs and trainers follow a well-meaning but misguided cycle of care that results in middling outcomes. Following this cycle, practitioners:
Select exercises randomly, hoping something will stick
Don’t collect data as the client trains or rehabs an injury
Fail to acknowledge when something isn’t working
Proceed with a plan even if the client isn’t improving
As a result, Fritz said it’s not uncommon to see athletes’ performance plateau or patients reinjure themselves because they were cleared to return to activity prematurely. Those suboptimal outcomes aren’t due to a lack of care or experience on the part of trainers and PTs – they’re a result of not leveraging data, according to Fritz.
He believes that, while many of his peers have good intentions, they rely on conjecture instead of numbers out of a “fear of not performing.” Unfortunately, in many cases, the fear of numbers not showing progress is what ends up holding patients back from making progress.
The Solution: Data-Driven, Evidence-Based Care
When Fritz opened his clinic, he vowed to let numbers drive his decisions. Instead of simply treating symptoms, Fritz follows the scientific method to uncover the root cause of clients’ pain or limitations – and uses the Kinvent ecosystem every step of the way.
Phase 1: Range of Motion Testing
Before a new client picks up a weight or trains with a resistance band, Fritz maps their entire body and “collects as much data as possible.” He uses Kinvent’s K-Move and K-Deltas to measure the range of motion of every major joint, from the neck down to the ankles.
Fritz then looks at the data to identify any deficiencies or major asymmetries. From there, he develops a hypothesis about what’s causing a client’s discomfort and devises a training plan to address the issue.
In addition to guiding his decisions, Fritz says the baseline measurements play an important role in keeping his clients – and practice – safe.
“I use range of motion to start because I don’t know if I’m working with a ticking time bomb,” he said. “If a client has a herniated disc in their neck or back, I don’t want to poke the bear or hurt them. It’s a huge liability, and I want to reduce those risks.”
Phase 2: Strength Training
Fritz continues to collect data with Kinvent sensors – including the K-Deltas,
K-Power and K-Pull – as his clients train. During this phase, he often uses the Kinvent App’s training mode to get numbers about clients’ speed, strength, power, stability and agility without pushing them past their limits. He typically starts most clients with light loads, then “uses training mode to build them up over the weeks and months.”
To keep track of how he set up tests (e.g., seat height or position), and how clients performed (e.g., whether they compensated or had poor form), Fritz uses the notes section of the Kinvent App. “Kinvent is the only company I know of that allows you to do this,” he said. “You can write in notes as you test. It takes some time to do, but it pays off dividends later on.”
Fritz also uses Kinvent like a “report card” to get big picture information. He monitors the data closely to see whether his clients are improving (thus confirming his original hypothesis) or plateauing.
Phase 3: Pushing Past Plateaus
Fritz believes that plateaus are a natural – and nearly universal – part of training and rehabilitation. “You’re going to see improvement out of the gate,” he said. “But you’re going to hit a wall. [That’s when] you really need to think about exercise science principles.”
To break through plateaus, Fritz says you need data – and a willingness to acknowledge when something isn’t working.
“One thing I’ve learned over the years is being cool with short-term failure or not winning,” he said. “If you find out that something doesn’t work, you can pivot and do something else and make decisions based on what the data says.”
CASE STUDY 1:
The problem: A 82-yr-old Sciatica client came to Science of Cardio with asymmetrical hips and poor range of motion in his left hip.
The diagnostic procedure: Fritz tested the patient’s hip range of motion by having him perform a sitting external hip rotation test with the K-Move.
What the data showed: Data from the K-Move revealed a 23% asymmetry between the client’s left and right hips.
The intervention: From July 2025 to May 2026, the client worked to strengthen his hip by completing rounds of low-intensity isometric holds on his side. Fritz measured Range of Motion and tracked his progress with the K-Move.
The outcome: After 10 months of training, the client reduced his hip asymmetry and gained better range of motion in his left hip.
CASE STUDY 2:
The problem: A 75-year-old with osteopenia wanted to regain bone density.
The intervention: Fritz had the client perform heavy, loaded back squats, starting with 45 pounds in March 2025. By September 2025, she was able to back squat 165 pounds.
What the data showed: After six months, the client got another DEXA scan. The scan showed improvement in several areas, including her lumbar spine, which increased by 3.8%. Other areas, like her femoral neck, didn’t show significant signs of improvement.
The next steps: The client is focusing on improving the bone density of her hips by performing heavy leg presses and jump training progressions with the K-Deltas.
CASE STUDY 3:
The problem: An 88-year-old with M.S. has difficulty walking and suffers from drop foot and poor grip strength.
The intervention: Fritz visits his client in his home twice per week.
To address his walking difficulties, the client completes five-yard “sprints” with his walking aid. Fritz tracks his progress with the K-Power, and the client tries to beat his score each week.
To address his poor grip strength, the client completes maximum voluntary isometric contractions using the K-Grip at five-second intervals.
Kinvent’s impact: Training with the K-Grip has helped encourage the client to continue to stay active. Every week, Fritz says the client tries to beat his score and that the data from the sensor is a “great way to get people to try to beat their personal records.”
Training with the K-grip improved the client’s grip strength for a time, but he eventually hit a wall. To move past the plateau, Fritz is now using Kinvent’s training mode to set goals for the client to reach.
The Results: Better Outcomes, Faster
Fritz’s primary mission is to make it easy for his clients to live their lives without pain. Kinvent has helped him achieve that goal. Instead of choosing exercises randomly and hoping something sticks, Kinvent data gives him a clear sense of what’s going on in a client’s body.
“Kinvent really helps out,” he said. “It gives a black-and-white direction to go in. The more clarity, the more specificity you have, the better.”
The ecosystem also gives him confidence that he is testing and training his clients as consistently and objectively as possible. The tools help him “eliminate testing bias” and “get consistent information.” That reliable data empowers him to make informed decisions about when to stay the course and when to pivot. As a result, his clients make measurable progress quickly.
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