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SAN JOSE — Life Time, a company that describes itself as an “athletic country club,” has signed a lease for a site occupied by Best Buy at Santana Row in San Jose, saying it hopes to open more Bay Area locations.

Life Time leased a building at the southeast corner of Stevens Creek Boulevard and South Winchester Boulevard for its fitness- and health-oriented country club, according to documents filed on May 28 with the Santa Clara County Recorder’s Office.

“Santana Row is a great location,” said Meghan Akradi, a vice president of real estate with Life Time. “It’s hard to do better than Santana Row in terms of iconic properties.”

The building at 3090 Stevens Creek Blvd. is 60,000 square feet and is two stories, according to commercial real estate database Property Shark. The initial term of the lease is 15 years, Santa Clara County property documents show.

“This is a market where we’ve wanted to be for many years,” Akradi said. “We hope to open our San Jose location during the first half of 2027. It depends on how quickly we can get permits.”

The only Bay Area location for Life Time currently is in downtown Walnut Creek at 1315 Broadway Plaza.

The future San Jose location is expected to feature an array of amenities, according to Life Time.

“We will have great fitness areas, fitness studios, personal training sites, yoga, pilates, cafe, work lounges, a sauna, and hot tubs,” Akradi said. “We will have a kids’ academy.”

Minnesota-based Life Time takes a different approach to fitness than is the case with typical health clubs in the country, according to company executives.

“Members are calling us a country club without the golf,” said Natalie Bushaw, a spokesperson for Life Time.

In March, Fast Company placed Life Time at No. 23 on its list of the 50 most innovative companies of 2025.

“It goes much further than a typical gym,” Bushaw said. “Life Time takes a holistic approach to health, wellness, recovery and fitness. People can literally spend all day at Life Time, and they do.”

Life Time operates at least 175 athletic country clubs that have an average size of 100,000 square feet, according to the company’s website.

“It’s like a Four Seasons resort every time you walk into one of our country clubs,” Akradi said.

The company has 1.4 million members and 41,000 employees. Life Time also has at least 150 kids’ academy locations.

Life Time executives believe the company’s approach can capitalize on post-pandemic attitudes to staying healthy.

“Our company has come back stronger than ever,” Akradi said. “We are exceeding our pre-COVID member engagement, member satisfaction and member attention.”

Over the one-year period that ended in March, Life Time posted a profit of $207.5 million, an increase of 182.3%, or nearly triple the profits over the similar 12-month period the year before.

Revenue over the year-long period ending in March totaled $2.73 billion, an increase of 18.6% from the same period the prior year, according to Finance Charts.

“People are clearly emphasizing their health and wellness,” Akradi said. “We feel well positioned to continue to serve those people.”

Originally Published: June 5, 2025 at 5:30 AM PDT

Garmin has been in the space of digital fitness watches for some time now and it has just launched two new models including the Forerunner 970. The Forerunner 970 kicks off the launch with a titanium body construction. It comes in the tonal options of gray, white and silver, or a soft gold color.

However, the colors are turned up a notch at the bands with a sportier touch of white and yellow on the white model. The soft old is detailed with gray and indigo on the band, and it all elevates the aesthetic with a dash of color. Details of the watch include its standard tracking along with the new Triathlon Coach feature.

Image Credit: Garmin

How the franchise built for growth from day one — and made a critical tech pivot to help accelerate it

When Roger Martin set out to build RockBox Fitness, he didn’t envision a single flagship studio with local buzz. He saw a national franchise brand — and he built accordingly.

“RockBox Fitness was built from inception to be a national franchise,” said Martin, founder and CEO. “We set out with the intent to build a concept that was scalable, repeatable, and had a market fit. Our first hires, before the flagship store was built, were two marketing consultants, a web designer, and an advisory board. We wanted to get the look, feel, layout, branding — everything — perfect right out of the gate.”

It was a bold move. But the early investment paid off, allowing the brand to grow deliberately, refining systems and testing market fit before offering franchise opportunities. Still, scaling a fitness franchise is hardly linear. The brand’s high-energy model and marketing-forward foundation attracted interest across regions — but behind the scenes, Martin was dealing with mounting complexity.

Manual royalty tracking, fragmented systems and inconsistent digital experiences began to threaten the very thing RockBox was known for: delivering a consistent, standout member journey across every location.

That’s when Martin made the call to migrate to a new business management platform — a move many franchisors avoid mid-growth. For RockBox, however, the risk of standing still was greater.

Tech as a Growth Lever, Not a Patch

“The risk of not making the move meant we would be operating suboptimally with our marketing efforts, our digital customer experience, and our ability to easily integrate additional tools, like our lead nurture software, ThriveMore Autopilot,” said Martin.

His choice: Xplor Mariana Tek, a platform known for helping boutique fitness brands streamline operations and drive franchise performance. But it wasn’t just about features; it was about strategic alignment.

Martin had a clear vision of what RockBox needed to scale: control, consistency and clarity. And as more locations came online, automation wasn’t just helpful — it became critical.

“The marketing suite and automations are outstanding and the fact a franchisor can push emails and texts down through the system is outstanding,” he said. “This gives the franchisor more control and honestly, makes it easier for the franchisee.”

A New Approach to Royalty Management

One of the most impactful outcomes of the shift has been RockBox’s use of Mariana Tek’s Franchise Fee Portal — a tool that eliminates manual royalty calculations by tying them directly to transaction data.

“This was a mandatory feature for us,” Martin explained. “Transaction-level royalty pull is critical to operating at scale. One, the franchisor always gets paid and doesn’t have to chase the franchisee for funds. Two, the franchisee benefits because they can plan their cash flow based off what hits their bank account. Nothing is ever taken out of their account after the remit is deposited because the royalties and brand fund fees have already been removed.”

According to Stephanie Parks, Product Manager at Mariana Tek, the portal replaces “fragmented, manual processes prone to delays and human error” with real-time automation and transparency. “Both franchisors and franchisees can log into the same dashboard, see the exact breakdown of what’s owed and why, and drill into reports by fee type, location, or time period. The result is a smoother franchise relationship built on trust, accuracy and efficiency.”

A Seamless Software Switch Without the Pain

Of course, even the best tech can backfire without proper onboarding — something Martin had experienced firsthand with past systems. But the migration to Mariana Tek turned out to be one of the smoothest transitions in RockBox’s operational history.

“It comes down to the Mariana Tek team,” Martin said. “They have been hands-on with us the entire time and are always professional, informed and fast. The software is outstanding, but the people are who make the transition easy and seamless.”

That “white-glove” experience is by design, according to Lauren Everett, Senior Director of Customer Onboarding at Mariana Tek. “We take the time to deeply understand each client’s unique business. Our onboarding isn’t about rushing clients through — it’s about tailoring the system to match their operations and goals.”

Scaling the Experience, Not Just the Business

While financial infrastructure is a vital piece of the puzzle, Martin emphasizes that brand growth hinges on more than numbers. For RockBox, every aspect of the member journey — from digital booking to the in-studio experience — had to be replicable, even in markets where ownership styles and staffing levels varied.

“The customer experience from the moment they book to when a prospect first walks in the door, the class experience and the sales presentation after is all standardized,” Martin said. “However, with staff turnover at the studio level and owners who are not as involved, it is an ongoing project to ensure all locations are providing the exact same, top-notch experience that makes RockBox Fitness so unique and special.”

Parks credits the RockBox team with treating technology as more than a back-office utility. “From day one, their leadership team has approached technology not as a back-office afterthought, but as a strategic lever for expansion and differentiation,” she said. “RockBox doesn’t just want tools that work — they want tools that evolve. Their team was quick to recognize the power of real-time financial automation and the operational lift it can provide. That kind of forward-thinking approach is why they’ve been such a valuable pilot partner for the Franchise Fee Portal’s newest capabilities.”

Lessons from the Field

RockBox’s evolution hasn’t been without growing pains. Early on, Martin learned that what works in one location doesn’t always scale cleanly — especially when people are involved.

“The lesson was that a higher ticket package ($2,500+) required a highly skilled salesperson, and that was challenging to replicate on a national scale,” he shared. “Most people in fitness don’t like to actually sell. That is not why they got into the field, yet it is a critical skill to grow a fitness business.”

Franchisee selection, too, required careful calibration. “We look for past records of achievement, a willingness to roll up their sleeves and do the work, a penchant for learning, and a strong bias for action,” Martin said. “If any of those four areas are weak or missing, we take a pause and either ask deeper questions or we pass on the candidate.”

Advice for Future Owners

For entrepreneurs considering the leap into franchising, Martin doesn’t mince words.

“Scale to at least three locations to ensure the concept is viable. And these should include a large city, a smaller city, and a suburban location to test various markets,” he advised. “Then a founder needs to ask himself/herself, ‘Am I ready to invest millions of dollars to grow this business knowing the ROI will not come for 5–7 years?’ To grow fast, you must invest significant dollars in this business.”

His advice to his younger self? “Are you sure you want to do this? Just kidding. I would tell him that it is going to get a lot harder before it gets easier, so know that going in and be prepared for this and it will be less stressful overall. This is true in scaling any business.”

Parks says she admires how RockBox balances brand consistency with franchisee autonomy. 

“Their leadership knows that to grow effectively, you need tight operational control paired with systems that franchisees trust,” she says. “Mariana Tek helps deliver that with centralized configuration, automatic payments and transparent reporting. Together, we’re not just managing growth — we’re accelerating it.”

Fitness trackers are everywhere these days on iPhones and watches. Fitness enthusiasts and those trying to achieve their goals use these trackers to count steps, track workouts, and measure calories. With so many people depending on them for reliable data, it raises the question: How accurate are those wearables? Are we really getting the correct data on our calories, heart rates, step counts, and more?

In a study published in the European Journal of Applied Physiology, the researchers note that wearable technology has been named the number one most popular trend, and this popularity is predicted to continue growing. Despite their merits and benefits, the researchers also highlight the significant limitations associated with the validity and reliability of the metrics measured from these devices. As they rightly say, advanced marketing doesn’t always equate to advanced technology. 

Recently, a professor and doctoral student at the University of Mississippi set out to explore the accuracy of these wearables. Here’s the scoop.

New research

In the new meta-analysis, researchers reviewed 56 studies that compared the popular Apple Watch to standard, trusted measuring tools for heart rate, step counts, and calories burned. They presented their study at the University’s Showcase of Research and Scholarly Activity session.

For this analysis, the researchers assessed the accuracy of the Apple Watch across different age groups, health statuses, and types of physical activity. They also tested various versions of the device. 

The researchers raised concerns because some people are using these devices to help guide decisions about their medical conditions or workouts, so it’s important that the data is accurate. Otherwise, inaccuracy could cause confusion, overtraining, or even cause some people to miss health warnings. 

The results

The results of this meta-analysis reveal that, overall, Apple Watches are accurate for measuring step counts and heart rate. The MAPE or mean absolute percentage error is a standard statistical measure that helps determine the accuracy of forecasts and predictions compared to actual values. Researchers calculated the difference between the data from the Apple Watches and the data from standard trusted measuring tools before calculating a “percentage error.” 

Here are the MAPE results:

• 4.43% for heart rate
• 8.17% for step counts
• For energy expenditure, the error increased to 27.96%, showing a more significant inaccuracy.

The researchers found this inaccuracy with all individuals and activities, including cycling, running, and walking.

The takeaway

The takeaway is that while Apple Watches and similar devices can be useful tools in your fitness toolkit, they shouldn’t be considered a replacement for sound medical judgment or advice and standard clinical measuring tools and technology. It seems that, particularly with values like energy expenditure or calories, Apple Watches might not provide the most reliable data. These nifty little devices appear to be better at collecting certain types of data compared to others. As we can see in this analysis, the MAPE for heart rate was only 4.43%.

That being said, big companies like Apple are refining and improving their technology, which will hopefully enhance accuracy in the future for those of us who want to use fitness trackers.

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If I told you that NASA has developed a radical new way of monitoring and quantifying your workouts, and that that method outperforms all others, you’d probably assume that it involves bleeding-edge science. There would be AI, and some sort of wearable or perhaps even injectable technology. It would be very expensive.

But you’d be wrong, for reasons that tell us something important about the quest to transform training optimization from an art into a science. A new study by Mattia D’Alleva and his colleagues at the University of Udine compares different ways of assessing the “training load” of different workouts—and finds that a low-tech NASA questionnaire produces the most accurate results. The findings offer a reminder that outsourcing our training decisions to wearable tech algorithms doesn’t always outperform simply listening to our bodies. The research also raises a tricky question: is the workout that makes you most tired also the one that increases your fitness the most?

Why Does Training Load Matter?

The goal of training is to impose a stress—a training load—on your body that makes it tired in the short term but triggers adaptations that make it fitter in the long term. Going all-out in one workout isn’t constructive, even though it imposes a huge training load, because it leaves you too tired to train effectively the next day. The art of training is figuring out what mix of easy, medium, and hard workouts will enable you to accumulate the greatest possible training load over weeks and months without getting crippled by fatigue.

In its simplest form (as I discussed here), the training load of a workout is a combination of how hard you push and how long you push for. But the details get tricky. What’s the best measure for how hard you’re pushing? You could use pace, power, heart rate, heart rate variability, lactate levels, perceived effort, or other progressively more esoteric metrics. And how do you combine effort with duration? You can’t just multiply them together, because effort is nonlinear: running twice as fast for half the distance won’t produce the same training effect.

The new study, which is published in the International Journal of Sports Physiology and Performance, compares seven different ways of calculating training load. Four of them are variations on a concept known as TRIMP, which is short for “training impulse” and is based on heart rate measurements, using equations that account for lactate levels, breathing thresholds, and other details. A fifth uses heart-rate variability, and a sixth uses a subjective rating of effort. (Most fitness wearables, by the way, likely use a combination of the above methods, though their exact algorithms are typically proprietary.) The seventh method is the NASA questionnaire, which we’ll come back to.

The gold standard against which all these methods were compared is the “acute performance decrement,” or APD. Basically, you do an all-out time trial, then you do your workout, then you do another all-out time trial. Your APD is how much slower the second time-trial is compared to the first one, as a measure of how much the workout took out of you. Obviously this isn’t a practical way of monitoring training, because you can’t race before and after every workout. But for researchers, it’s a way of checking whether various methods—including the seven they tested in this study— correspond to the reality of how hard a workout is on your body. At the end, they were able to figure out which method was the most reliable predictor of training load.

What the New Study Found

D’Alleva and his colleagues recruited 12 well-trained runners (10 men and 2 women) to test four different running workouts on different days:

• Low-intensity training (LIT): 60 minutes at a pre-determined comfortable pace
• Medium intensity (MIT): 2 x 12:00 at a moderate pace with 4:00 easy recovery
• Long high-intensity (HITlong): 5 x 3:00 hard with 2:00 recovery
• Short high-intensity (HITshort): two sets of 11 x 30 seconds hard, 30 seconds easy

The performance test was running at VO2 max pace until exhaustion. When they were fresh, the runners lasted just under six minutes on average. After the one-hour easy run, their APD was 20.7 percent, meaning they gave up 20.7 percent earlier in the post-workout VO2 max run. After the medium-intensity run, the APD was 30.6 percent; after the long intervals, it was 35.9 percent; after the short intervals, it was 29.8 percent.

So how well were each of the seven training load calculations able to predict this APD? The short answer is: not very well. Here’s a comparison of APD (on the left) and one of the parameters studied, which is called bTRIMP and is based on heart-rate measurements and lactate curves:

 

In fact, the relationships are completely reversed: the easiest workout according to bTRIMP produces the biggest APD in reality, and the workout ranked hardest by bTRIMP produces the smallest APD. All except two of the training load calculations the researchers measured have similar upside-down relationships. The two exceptions are heart-rate variability and the NASA questionnaire, which look like this:

The heart-rate variability measures, on the left, don’t tell us much, because they’re basically the same after each of the four workouts. (You can see some subtle differences, but they’re not statistically significant.) The NASA questionnaire, on the other hand, bears a striking resemblance to the APD data, and the statistical analysis confirms that it’s a good predictor. In other words, it’s the only one of the seven calculations tested that, according to this study, accurately reflects how exhausted you are after a workout.

So what is this questionnaire? It’s called the NASA Task Load Index, or NASA-TLX, and was developed in the 1980s. It’s simply a set of six questions that ask you to rate the mental demand, physical demand, temporal demand (how rushed were you?), performance (how well did you do?), effort, and frustration of a task. You answer each of these questions on a scale of 1 to 100, then the six scores are averaged—and presto, you have a better measure of how hard your workout was than your watch or heart-rate monitor can provide.

What the NASA Questionnaire Misses

These results don’t mean that we should all start recording NASA-TLX scores in our training logs. Questions like how hurried you felt don’t seem very relevant to running, or to training in general. What’s more significant about the questionnaire is what it doesn’t include: any measure of how long the workout was.

All the other training load measures rely on a combination of intensity and duration. But the effect of duration swamps the measurement: that’s why the bTRIMP graph above shows the 60-minute easy run (LIT) as the workout with the biggest training load. It’s really just telling us that it was the longest workout. The NASA-TLX, on the other hand, just asks (in various ways) how hard the workout felt once it was done. That turns out to be a better way of predicting how much slower you’ll be after the workout.

There’s an implicit assumption in all of this discussion, though, which is that the workout that provides the biggest training load is the one that will improve your fitness the most. Is APD—how much slower you get over the course of a single workout—really the best predictor of fitness gains? It’s easy to come up with scenarios where that’s not true. If I sprain my ankle, my APD will be enormous, but that doesn’t mean I’m going to be an Olympic champion next month. Similarly, you can imagine workouts that would inflict a disproportionate amount of performance-sapping fatigue—steep downhill running, for example—compared to their fitness benefits.

Perhaps what we’re seeing here is not so much “good” (NASA-TLX) and “bad” (TRIMP) measures of training load, but rather good measurements for two different types of training load. The APD and NASA-TLX mostly reflect how hard/intense/fast the workout was. TRIMPs and other metrics that incorporate duration end up mostly reflecting how long the workout was. There’s no reason to assume that these two parameters are interchangeable. It’s not just that you can’t get the same training benefit by going twice as fast for half as long. It’s that there’s no equation that makes fast running produce the same benefits as slow running. They’re two different physiological stimuli, and the smart money says you need both to maximize your performance.

So where does this leave us? I’m not anti-data, and I’m open to the idea that some of the newer metrics provided by wearable tech might reveal useful patterns if you collect them consistently. But if you strip training down to its bare essentials, these results suggest to me that there are two separate parameters that really matter: how long and how hard. And for now, I’m not convinced that we have any measuring tools that are significantly better than a stopwatch and an honest answer to the question “How did that feel?”

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