By Carl Valle
Every week I get the same plea to explain how to improve compliance with athletes— mainly with diet and sleep, but monitoring as well. No matter how much research coaches read or years of experience they have, a limiting factor to athlete success is the athlete themself. Instead of writing a great theoretical piece on monitoring or tips on sleep and sports nutrition, I wrote the article that everyone needs but rarely asks for publicly.
This guide on compliance deals with the reality of coaches’ experiences. I understand because I, too, struggle and succeed with getting athletes to do what they need to do. The promise is simple; what is shared works for me and other coaches. How much you want and believe in the data you collect will dictate what you get back from athletes.
The Core Reasons Why Athletes Are Not Compliant
Some athletes are super-dedicated, some are rebels, and most will do just enough to get better. In general, athletes want to play or compete, and training is just a means to an end. Monitoring, which is repetitive and not engaging, is seen as a very distant means to an end and that’s why it fails. Additionally, while the data that the coach or sport scientist asks for is designed to help athletes, it’s really most helpful to practitioners because they know what to do with it. Finally, the entire process is a brain drain, meaning it’s mind-numbingly repetitive and the user experience is just not enjoyable.How much you want and believe in the data you collect will dictate what you get back from athletes. Click To Tweet
Of course, other reasons exist for athletes not wanting to share their data or get data. Beside it being a literal pain at times, it’s a privacy issue too. When data is extracted from someone, there’s a feeling that they were violated a little. If the data is used to make decisions that might not align with the athlete’s goals, then barriers start to form. If companies and coaches really understood the reasons that data is so hard to collect, we would be seeing much different procedures and far better products on the market.
All things being equal, if a coach can get their athletes to do what they know works better than the competition, they will have an advantage. Rarely I have I been limited to a training system or had a lack of available knowledge; it’s usually about whether the athlete will sacrifice a little to adhere to a good coaching program.
If companies and coaches really understood the reasons that data is so hard to collect, we would be seeing much different procedures and far better products on the market.
While some coaches are as charismatic as a local politician and can sell athletes on the importance of data collection, even the popular ones reach a limit on modifying behavior or motivating the lazy. You can read all the leadership books to develop your “emotional IQ,” but these approaches often fail because of the human condition and the fact that monitoring is not the most entertaining part of sports. In order to get athletes to buy in, make the sale easier instead of focusing on marketing or closing techniques.
Friction Points to Player Monitoring and Data Collection in General
When creating workflows for any data collection process, ask the most blunt questions first in order to make the process streamlined and smooth. No matter how great the strategy is in the office, the rules change when in the field and often require many iterations. Over the years, I have polished some of the things I do, but the early rounds when I was just getting started were honestly ugly. The growing pains of innovation create a lot of scars and cautionary tales, but it’s worth it when the process evolves into a well-oiled machine. Here are four friction points to the collection of monitoring data that everyone talks about but never seem to change.
1. Culture – If I see one more social media post on how important “culture” is, I will flip my lid. Sure culture matters, but what power does one person have with it? If the tide is against you, the results are limited. If the team is going to lose a lot of games, morale will be limited and compliance will be a problem. Remember that winning is a product of talent and coaching, and sport science just sprinkles in the odds of success.
Many coaches cheer for teams that are healthy and prepared due to great culture, but eventually the honeymoon wears off when results are not tabulated in the win column. Sometimes winning has a cost as well, as better teams usually play more games and that means less “off” time and more pressure. It’s smart to move the culture change forward; just remember not to be a casualty, as anything different can make you a pariah.
2. Policy – Similar to culture, policy is putting your team’s values into written language. More than 10 years ago, I was an assistant to a high school team that put their core values into a contract. This backfired because the head coaches made it a top-down process and didn’t include the athletes, so it looked like restrictions versus the support guide that it really was intended to be. Still, if the rules or requirements are not written down in some form for your team or organization, the entire process breaks down quickly. Selective memory of the unwritten word is one of the reasons that conflict occurs, because it becomes a personal war versus everyone following a blueprint for success.
One of my former athletic directors came up with an athletic handbook that helped turn our program from one with nobody qualifying for the state meet into one that won conference titles and put athletes on the podium a year later. When the document is a composition of everyone’s goals and form procedures and policies, it’s a living commandment to winning that eliminates debate and confusion.
3. Acquisition – Monitoring, or even simple data collection from testing, boils down to how well you can get information from a short event, like an HRV reading or a practice RPE session. Obviously, the science and validity must be very tight, but the workflow also has to be very organized. Athletes are impatient creatures and are often treated like royalty, so their perspectives are very warped from reality. If the user experience with training and monitoring is not rewarding and engaging, the sustainability of monitoring will die on a very short vine. I have data from years with athletes because the process was valued and actually enjoyable. I explain the way to make the boring and sometimes annoying process of monitoring better later in this article.
4. Time – The lack of time and need for quick methods of collecting data is a stark reality that plagues everyone in sport. By nature, the value of training is simply not a priority at professional levels, when tickets are to be sold. Therefore, everyone in sport, even at low levels, has precious little time available to do much. Budgeting for priorities with time could be an entire book or seminar series on its own, but a basic, and accurate, way of looking at the challenge is doing the simple math of what creates the best return on investment.
Monitoring takes time to prepare, collect, analyze, interpret, and act on the data, so even something simple like perceived exertion or vitamin D screening is a surprisingly demanding task. Always be thinking of reducing the time needed by cutting out inefficiencies, not by cutting corners with science. Sports data management workflow is not about cheating the scientific process, but adhering to it in a clever and creative way.
All four areas are not impossible to overcome, and coaches should only worry about what they can control. Remember: Being honest about the monitoring process can feel very lonely if you are the only one putting the time into doing it right, but stick with it because it will pay off more than you know down the road. Appraise the four friction points and be critical of your own program so you can find gaps or areas you can improve. You will be rewarded for putting in a full effort and not giving up when everyone else is sharing opinions and you are the only one with an opinion supported by objective facts. Leadership doesn’t come from blogs or books; it comes from doing the right thing and applying it in a way that others value and respect.
How to Pull Compliance out of a Nose Dive and Break Into the Stratosphere
Any time a coach wants to improve compliance with athletes by making tweaks or small changes they are likely to fail, just fail a little bit later in the process. My biggest mistake with monitoring was trying to take science and make it more applied to the environment I was in. Bad thinking. Trying to improve compliance traditionally is just putting lipstick on a pig, and coaches need to go back to the drawing board and rethink the entire process. Applied sport science is not for coaches to take research and make it work outside the lab; it’s about taking the existing mindset and behaviors of athletes and embedding data collection with a little entertainment value.
Moving the needle is OK in some areas, but monitoring needs to shake the compass like a Tyrannosaurus Rex walking around. If I had to summarize this article in one sentence, it would be to take what athletes already love to do first and make that part of monitoring, instead of forcing something that they may not enjoy. Why don’t teams emulate the lifestyle patterns of athletes and make the process just a little bit engaging?
If I had to summarize this article in one sentence, it would be to take what athletes already love to do first and make that part of monitoring, instead of forcing something that they may not enjoy.
My first wake-up call with improving compliance was with the work of Natasha Schüll, a brilliant professor at MIT who I met at a Quantified Self meeting years ago. Her work on addictive behaviors with digital age gambling was a eureka moment for me: Why fight compliance when you can tap into addiction and throttle down? Focusing on pleasure and enjoyment was a far better starting point, provided the process didn’t cause some sort of disorder or place an athlete in emotional harm.Start the process right by thinking of user experience & ergonomics first, then data second. Click To Tweet
It’s not rocket science—if the process is dull and annoying, why would anyone participate? Make as much of the process cool and rewarding, and try to minimize the stuff that simply can never be a party to what is essential. It is worth repeating, start the process right by thinking of user experience and ergonomics first, then think about data second. Don’t be the bad guy, as plenty of opportunities for that will come on their own.
Building the Right Framework for Monitoring
The foundation for monitoring consists of valuing the data, then collecting it intelligently, sharing the information, and reinforcing its use. Most of the teams I work with do two of the four well, and completely miss the other two. Some coaches spread their talents over all four parts, but don’t deliver the pop! of making the user experience engaging, as monitoring is human, not just a giant math equation.
When monitoring athletes, make sure the timeline from wake to sleep to wake again is perfectly outlined. Coaches need to think about the daily lifecycle more than the training cycle in order to manage a very chaotic environment, and then try to scale it with a team or even entire college. What I will outline is standard, but the difference is that I will be upfront about what collapses because of things most coaches hate to admit.
Data Value: The cost of the data and what it’s actually worth are not the same, so when looking at budgets, look at the value of the information, not just the initial price of the system. For example, while getting a force plate is going to provide excellent force analysis, the value is not high if you struggle to interpret the force time curve and only want jump height. Subjective questionnaires may be free when using a Google form, but sterile options tend to kill off quality feedback and are not “sticky” with athletes over time. Monitoring hydration has value, but only when interventions and policies are in place.
Finally, the athlete has to understand the data just enough to value why it’s captured. If you don’t explain simply to athletes what you are doing with the data, they will not be very cooperative. Don’t make the mistake of too much education of athletes, either, as it leads to overkill. Athletes need an elevator pitch explanation, not a full trilogy movie.
Collection Engagement: The most bizarre failure of monitoring is treating athletes like lab rats and not customers. While I don’t like coaching and sport science acting like digital butlers, some understanding of what it’s like on the receiving end of monitoring is needed. Remember to “eat your own dog food” with monitoring: If you don’t do it yourself, you will likely not create buy-in with it later from your athletes.
One of the most powerful ways to connect to someone is to share. Sharing your own data is very intimate and shows that you care and believe. It states that it’s important for athletes to follow your footsteps. Engagement comes from experiencing points of friction or impairments in convenience and terminating anything that gets in the way.
Conversely, when possible, embrace the process and go with it. For example, the collection of HRV is often difficult to tame, but if it’s truly important, make an HRV room full of lounge chairs in dim light along with headphones for absolute quiet. Who doesn’t like a nice break from media, fans, and even family and friends? In this day and age, with so much literal visual stimulation and actual noise, a sanctuary is a nice respite from modern society.
Visualizing the Analysis: Data sharing ranges from a recognition that a sample was received to providing a rich tapestry of data to be charted for the athletes. Some athletes just need a check mark, while others may want to take the process further. I have mixed feelings about data transparency, as some athletes get too emotionally charged when they see numbers; but on the other hand, hiding information isn’t my cup of tea. What I do know is that coaches, who are responsible for loading and helping athletes recover better, see the data as a guessing game. This is fine for youth sport, but lousy for managing expensive bodies. Even a youth athlete with no hope of moving to the next level deserves the same type of care as elites, since no person should walk with a limp later in life because everyone was going by “feel” or similar.
Reinforcing with Action: Nothing strengthens participation in monitoring more than seeing a staff member use it in action. Action is where monitoring matters and that’s why most monitoring programs leave athletes in the dark, because the organization fails to implement a real intervention.
Common Data Sources and Uncommon Solutions
Most monitoring data is best captured during non-training hours, like sleep and HRV. Some monitoring options are done while training, such as session RPE and player tracking devices, but most monitoring options are about getting information that the coach needs but doesn’t have access to. Coaches want to monitor the training load objectively and subjectively, and the response to the training load both internally and externally. Each contextual need for data has a corresponding way to get athletes to buy into the value and burden of collecting it.
I see six categories of data type, based on the means used to acquire it. Some of the categories overlap a little, but for the most part they are unique data sets that must be supported with unique methods of keeping the process under control and ensuring it is not a pain for athletes to be involved with.
1. Subjective Wellness Indicators: Wellness scores are cheap, but you get what you pay for. Take the information with a grain of salt. Coaches who rely on subjective questionnaires have the data to show possible problems, but eventually a time will come when sustained problems have to be bumped up to objective diagnosis. Inputs are usually done via smartphone for streamlined convenience, and statistical analysis reveals possible problems.
While the data is useful with subjective questionnaires, coaches must treat them as pathways to service, and use the data for record-keeping. Why go halfway with questionnaires and not perfect the process with access to team services that can improve the scores? Have all questionnaires immediately connect to booking online for actions, not just send the form scores to an Athlete Management System.
2. Objective Sleep Data: Not getting objective sleep data is an easy excuse: Many teams say they value sleep but they can’t get athletes to “wear” something. While this is an understandable excuse, never give up on sleep as many options exist with this space to get data that is better than wellness indicators from a smartphone. Data integrity ranges from binary options to something akin to 93% of a sleep lab.
A common belief that sleep devices measure intimate information and violate privacy rights always foolishly comes up as a way to dodge not having the hard data on rest. No device is recording video and only when the athlete is sleeping do the devices really trigger readings that matter. Accelerometers are limited and, with any activity before bed rest, the key is the quality of sleep and length of it, not what happened before it.
A chicken and egg paradox exists with sleep, with many coaches thinking better sleep helps athletes recover from training, and some coaches noting that better training helps with sleep. Without having the external load, internal response, and sleep data, teasing out the truth is hard. Instead of avoiding the obstacles and hoping that athletes will put in an effort, make sure you go hard and talk about those barriers upfront and early. Explain to them that objective sleep data does not interfere with privacy, and those who compromise sleep and don’t wish to share their data will be doomed.
Having no hard line with sleep will be a bigger problem down the road, so have reasonable policies for expectations. Politically, athletes can give any information they wish, so if players want first-class support they need to give first-class information. Purposely create classes of services by the level of data they give so a cause and effect exists for non-compliance. You can’t make interventions blindly, so data has to be shared to get “premium” support. If you give in and go the extra mile, the athlete will do the opposite, knowing they will get the full treatment regardless of their participation. Sometimes learning the hard way will teach athletes, but again, this is up to the coach who is in the trenches.
3. Nutrition Logs, Body Composition, and Biomarkers: Frankly, diet is the hardest to measure since we are a long way from a consumer-grade implantable sensor to see what is going into the mouths of athletes. Instead, proxy options like reported food diaries (subjective), DEXA scans, and blood testing are a great start. Body composition is underrated big-time, since there are very few arguments over the interpretation of the data. How often one tests and how they measure is key.
One of the issues with body composition data is that it doesn’t create a training effect. Another is that the passive information requires the potentially awkward process of an athlete undressing and sometimes getting pinched with calipers. Also, the data is very humbling and might be a problem for athletes who are prone to eating disorders. However, avoiding body composition is just putting your head in the sand. Athletes that are not weighed could have potential health risks from dehydration and even be improperly loading their body in training because of inaccurate calculations.
Logs can range from apps all the way to a simple check-box rating of the day as a whole. I prefer habit tracking since most athletes can juggle only a few simple tasks, such as taking a supplement, eating their lunch, or getting the required fluid replacement. In my opinion, blood is a wonderful deep dive into patterned eating, and athletes are far more compliant when biomarkers are connected to food and their goals.
4. ANS and CNS Fatigue: While heart rate variability is popular, like sleep, it is very difficult to get athletes to leverage the information because collecting it is borderline magic. Some systems require less moving parts, like HRV4Training and ithlete, but measuring CNS fatigue requires time or expensive devices. Pupillography is going to explode when the sensor cameras become consumer-sized and smartphone-ready, but teams can get omega potential from Omegawave.
Most coaches share the issue that if an athlete is to do any measurement on their own, they will not get an accurate reading or they will sabotage the process by losing equipment or claiming it wasn’t working. Those claims are sometimes true, but if an athlete doesn’t value the data, they will never put the effort in. Also, athletes try to game data in order to control their performance and training loads themselves, as several coaches have noted. As someone who has years of data and is colleagues with athletes who have years of data as well, I think the key is not education but directing motivation.
I will write more about HRV measures, but athletes who give more than subjective data and either come to the facility or do the readings at home need to be rewarded in some small way. Training programs also need to have a little “vibe” for those who give physiological monitoring data in order to differentiate. Only when athletes feel like they miss out on getting the premium service do they want to add monitoring, and good science can help them feel the loss of missing out on more precise and custom training.
5. Muscle Readiness: The readiness information you can get from instrumentation like tensiomyography and thermography is very straightforward, but the majority of soreness and stiffness will come from subjective questionnaires. I made muscle readiness a separate standard because direct measures of tissue and what the tissue can do (performance testing) are going to grow. It’s better to prepare now than be thrown to the wolves quickly when the data becomes ubiquitous in a few years.
I mentioned earlier how EMS systems can either collect direct or estimated muscle readiness, but in the meantime, a good body chart with a soft tissue therapist worth their weight in gold will be a good starting place. Passive measures of muscle function will likely come in the next year or two, but active options like jumps are more performance management than recovery or fatigue monitoring. Motivation or volitional demands of tasks can pollute jump tests, so make sure you involve willingness-to-train metrics and soreness scores with any jump or power test. Like the subjective questionnaires, make sure the data feeds into training goals for muscle groups and recovery modalities that can create a temporary prophylactic response.
6. Training Load Sensors: When athletes are physically present, they are more likely to comply with wearing sensors on their body. Like any wearable device, comfort is king. Year after year, I see companies making products that are cumbersome, obtrusive, heavy, and difficult to manage. I realize that miniaturization is expensive and not all measurements are easy, but the first thing companies need to remember is that, if measurement isn’t easy and reliable, the product will die no matter how great the data was.
Even heart rate data during training isn’t easy, since a strap can be annoying and fall off, so some type of smart fabric product will dominate the future. Invest in using wearables only a few times each week so they’re cleaned and charged, as daily use might be too much for unwilling data. If you have a comprehensive program, it’s likely that you are covering the bases enough that you will not have any data gaps.Sport technology needs to be easy & reliable or the product won’t be used. Click To Tweet
In no way is the above suggestion list complete or perfect, but the goal is to be like Hannibal—find a way or make one. Use your own judgment and keep thinking about making a better mousetrap by looking for friction points and communicating to the athlete so they can share honestly what the pros and cons of the process are. Remember to share feedback from the athletes with the companies. Most companies will claim they don’t see the problems because many teams don’t actually collect the data they claim to at conferences, so be firm and work on making the monitoring products or services better with constructive advice.
Start Monitoring Smarter
The shared suggestions will help to improve the outcomes of monitoring enough to get started with something small, like subjective indicators. Larger and more-extensive options require more upfront work, but when infrastructure exists, the process runs pretty much on its own. Read this article a few times and refine the process so you yourself would intrinsically enjoy the monitoring process, not because the data is important. As more clever coaches share their tricks of the trade—after they actually know the trade—monitoring will become more practical for the rest of us.