60+ Things Our Check-In System Catches Automatically (So You Don't Have To)

Full transcript

All right, coach, strap yourself in, we've got a lot to cover on this one. There are 60 plus patterns that we detect within the check-ins and coaching systems that we build for our clients. It's not just limited to check-ins, because we have other daily things that run. Some of them are in Trainerize, some of them are in Coach Catalyst by reverse engineering the API, some of it is inside Airtable, and it all depends on what it is the coach is looking for too. But here's some of the stuff that we track automatically.

One of them is just making sure that when we're looking at the overall trends of clients, we only look at the full days completed. So if they just checked in now and it's 9am and they had one meal tracked, we only look at the days previous, so it's actual proper real data in there. We're able to see how many workouts they've done versus how many they were supposed to have done, automatically. We're able to see the trend of their body weight, so not just a day-to-day change, but overall trends of the scales from week-to-week averages, and we pull that in automatically. We're able to see the deviation of the average when it comes to the swing of nutrition, where every day we're within about 5 to 10% of the same calories, but then on the weekends we're way over. We're able to identify that and show it to the clients automatically. We're able to catch late syncs to their tracker as well. So it's like, oh, what if I just didn't sync my watch? We can catch that kind of stuff too, so don't worry.

And then we're able to catch PRs and things with their training. This one specifically is in Trainerize: when they do their check-in, they'll see all of their recent personal bests, training volume, new highest weight, new one rep max, five rep max, ten rep maxes, all that kind of stuff shown to them on their actual check-in, so they're able to see the strength increase over time.

Then when we have a look at nutrition and tracking, we're able to do some really good maths, because we have the nutrition tracked, but we also have their body weight, we have their wearables, we have their step counts, and we're able to use all these calculations together to see, right, are they actually eating what they're saying they're eating? For example, someone might have logged 2,300 calories, but based off the body weight change and their steps and their height and their age and all these other calculations, we reckon they're actually consuming about 2,600 calories. Then there's other stuff. If they're in a deficit and way under their calories, that's not necessarily that much of a bad thing. If they're intentionally in a surplus and way over their calories, that might actually be a bad thing. So it's contextual to their actual goal, being over and under and by how much, whether that's actually going to be an issue, and whether it flags it to the coach.

Then we have logging consistency. It's able to see, out of the last seven days, how many days did they actually log? Of those days, were they fully logged or partially logged, where they only logged maybe one to two meals and were under the calories? With the nutrition, if you are, say, 9% over or 17% under, what's going on? That changes depending on whether you're on a fat loss goal, a hypertrophy goal, a maintenance goal. All of this is contextual to where the client is actually at. Then if somebody is in too much of a deficit, it will give off a warning, because yes, that is the ceiling of the calories, but too much below that sets off a little flag for the coach to be aware of it as well.

Then the weekend swing on calories: being able to see the difference between the weekly calories and the weekend calories and figuring out whether that's actually intentional or not, whether they're calorie banking, and then setting up follow-up questions for the clients based off that. You'll see the calorie banking in there as well. Then we have the protein under target, and we're able to identify that, and we're able to track the quality bucket as well. So if we're having a look and Monday to Friday they were tracking their breakfast, their lunch, their dinner, and then on the weekends they were just tracking one meal for the whole weekend, all bunched in together, chances are they're going to have forgotten things. It looks like they're trying to track their entire day in one go, and it's possibly a diet recall they're doing, where chances are they're going to miss things. So it will flag that for the coach: take it with a pinch of salt, just in case this is not exactly what they said it is. That's another flag for the coach as well.

Then when it comes to body weight, it will celebrate if they hit a new lowest weight in their fat loss phase. We can identify that this is the lowest weight they've hit in this fat loss phase, and give them a little celebration moment when they're in their check-in. Then, if they're in a deficit or a surplus or whatever phase we're at, we can set certain ranges of how their body weight should be changing, up or down, and how fast it should be changing, and then see whether they're above that, below that, on track, on target. We're able to identify sharp drops in body weight. We're able to see stalls in their body weight, and stalls in the gym as well, where one lift is not progressing. We're able to see if the weight is actually moving up in a deficit, moving in the wrong direction, which would be flagged and put onto the profile. And then we're able to see the on paper versus the on scale: the difference between self-reported data and actual data, and where the mismatch is coming from.

Then we have the adherence awareness: whether they're aware or unaware of how close they're being to what they should be consuming. Big context on this. If somebody is saying, yeah, I'm absolutely dialled in on my nutrition, I tracked everything, I did all this, I did all that, and then we look at their actual nutritional data, and it says here we had porridge, but it doesn't say whether we had milk or water in there, and dinner looks like it's probably missing some ingredients, chances are we're not tracking everything we need to be tracking. So that would be a little bit unaware of what we're actually doing, and what they're feeling isn't quite matching the actual data itself. It's able to flag all that kind of stuff, and then you're able to see where you are versus where we want you to be.

So we can set roadmaps. Normally coaches do this on a Google Sheet. They have a roadmap of, oh, you're in a fat loss phase to here, then we're going to here, then we're going to here. We can actually get their check-ins to automatically correlate with that roadmap. At the very start, this is where we started off on day one, and then as the weeks go on, you'll see the trend line of where we want them to be if it was just a linear line, and then where their check-ins are actually corresponding to that trend. And then you're able to see it phase by phase by phase, which is all automatically tracked. You don't have to track this for the clients, and it can be visible to the clients all the time, so they can see their phase, the next phase, all that kind of stuff.

Then training and recovery. We're able to see what's going on with their training adherence, so are they actually doing the training? We're able to see when they're reporting it as an eight, "oh, we tried really hard, we were training this week, I did an eight out of ten", but then we check the actual workouts, and the individual workouts are rating the RPE at, say, a 6.2 on average, and no progressive overload is happening. We probably need to train a bit harder. But for the client to see that, the coach probably needs to get them to record a video and then actually say to them, we stopped here, you should have done this, this and this, and get them to give you information so you can coach them on that. That is a flag that happens automatically. If the lifts are not moving, it will flag it to the coach as well. If most of the lifts have actually stalled, so if only two of the nine main ones, let's say, that client is doing are progressing, it will flag that to the coach. If there's a strength stall, it will have a look at other stuff: yeah, the strength isn't going up, but to be fair, we're only hitting our protein 74% of the time and our sleep is consistently a five out of ten, not great. Recovery is likely the issue, so let's focus on the recovery first and then we'll see if the weights actually start moving up.

We can see readiness from the HRV. If they're using any sort of a wearable that's synced into the coaching apps, we're able to see that data on there as well, so we can pull in some of the recovery metrics and it adds more information to the overall picture of pattern detection. We're able to ask them then about joint pain: we're having joint pain, and the progressive overload isn't happening as well. Maybe we should focus on fixing why we're getting the joint pain before we focus on the progressive overload stuff. And then if we notice anything that's consistently happening on the same day every week, for example, every Thursday you seem to not track your calories, what on earth is going on on Thursday? It will ask you those questions.

Then with movement, we can see stuff like the step average. We can see if their steps are starting to reduce over time. We can see the difference between their daily steps and their weekend steps. And we can lift the target on their profile with quick actions: one click, and it will go onto their Trainerize, for example, and set a habit to increase the steps from there.

Then habits. We can see, right, the habits: 10,000 steps per day, hit Monday, Tuesday, Thursday, and the rest of the days we didn't hit it; the water we hit; the protein. This is custom per whatever habits the client is tracking, but we can actually show them their habit consistency, just to make it really clear what they are and aren't doing. We can see overall habits at 62%. We can see that one of them is consistently causing issues compared to the others, and use triaging, greens, ambers, reds, and try to fix the one that's red first, obviously, then the ambers, then the greens. And then we can show them streaks: hey, you're on a six out of seven days streak, let's keep this going, and reaffirm the stuff they're doing well, not just telling them the stuff they're not doing too well. And we can see how much actually got ticked off.

Then we have the intelligence layer. This is where we take all this information and put it together, and this is the adherence and risk scores. Do we actually need to change the plan? Do we need to just get more adherence? Do we need to check with the person whether it's sleep or recovery or something else? All these metrics combine into it: the training, the logging, the deviation of how close we are to the actual calories, the protein intake, the steps, the sleep, the check-in photos, how happy you are with your progress in them. That will help us create this matrix. If we're doing really well and all the things are matching, we're making progress, moving forward, we're happy, keep doing what we're doing, we're green. If we're high adherence and getting poor results, something is off here, and it will tell us whether it's the plan that needs to change or the person that needs to change. If we're low adherence, it's going to tell us we need to fix the adherence, and to do that we might need to adjust the plan, or we might need to just adjust the targets so the person can be more adherent. And if we're not being adherent and still getting the results, we're essentially working on borrowed time. This is going to stop eventually and we'll end up in one of these quadrants, but it will figure that out for you and it will help you coach through that.

Then some other stuff that we spot: weekend overeating patterns, the tracking quality pattern, and how stress and nutrition move together. So when your nutrition is always rated lower, your stress always seems to be rated higher. And then in the client's check-ins, they can look back into their other check-ins and see the patterns of what they've been rating over time as well. We'll spot low protein patterns. And then we actually get people categorised based off that stuff as well. And then we have extra flags: this person is not being adherent with this, this and this, maybe we do a midweek follow-up about X, Y and Z to make sure they stay consistent with that.

Then the risk detection. This one is still pattern detection, but what we're trying to do is identify the patterns that lead to somebody churning. The first one: if their progress satisfaction is rated a three out of ten, which they get asked in their check-in after being shown their before and after photos, that adds some points towards risk. Then if the last two self-rated check-ins were both red, that's a lot more points towards it. Then if their logging is starting to slide, from six days to four days to three days, that's going to be a few more points towards risk. Then if their last app activity was, say, six days ago, where they haven't even logged into the app, that is another few points towards risk. And the last one: if their overall adherence across the training, nutrition, all that kind of stuff, drops below, let's say, 50% or 60%, or whatever thresholds you want to set, that would be more points towards risk. And then you get actual risk scores on the client. So you'll be able to see, hey, this one is very high risk, and we should probably go intervene. This person is at risk of checking out. This person is not happy with their progress. This person is really happy and wants to push even further. And then you have all these other flags that come into play.

Then you have stuff for when the story doesn't match up with the client, where they're like, yeah, I'm doing everything, I'm green, I'm doing really, really well, and you're like, yeah, the data is not saying the same, and you can show them that actual data. So we're seeing the self-reported versus the actual one. They said the nutrition is on point, they rate nutrition eight out of ten, but the weight is not moving. Right, well, maybe we're not actually tracking everything we say we're tracking, or we need to adjust the steps or the deficit, whichever way we're going to do that. Or they say the movement is great, but the steps are actually low on the data tracking. Right, well, their perception of great with the movement might need to be changed, we might need to adjust the targets, and then show them how we can help with that. Then you have the skill mastery. If you have different levels of skills, and we do this, this and this, it will prioritise: they're doing all of this, but this is the one they're getting stuck on, and this is what we can do to help them with that. It'll help you coach them through that actual skill and help them move through that skill as well. And then you have some more intelligent layer tracking to do with that.

And then the last part: what can it actually do when it recognises these patterns? Well, the first thing is it can automate some of these patterns for the client. If it's stuff like they haven't done their progress photos in a while, it's going to say, do your progress photos, and it's going to get them to say "I'm going to do it" in their check-in, and then it reminds them in there. The same way, if they're not tracking their body weight consistently, it's going to say, hey, don't forget, jump on the scales most mornings just so we have cleaner data to view on that trend line. So some stuff in there we can automate, where it's not going to affect or contradict what the coach will be saying to them and the way that you coach. The next part is it can do some admin. If we need to follow up with this person about something, it can set it inside our task list to follow up with the client about it, or put a flag on their profile in your CRM. The next part is it can actually help you with suggestions for the coaching. It could be, hey, why don't you send this to the client, or, based off the calculations, we should probably reduce these calories, and it can give you suggestions and then actually help you do some of it. With Trainerize, we can say accept the changes to the nutrition, and they'll go and change on their actual profile. Or if something is really flagging, and you're not the only coach in the business, it can send it off to the senior coach or the owner: hey, massive red flag from this client at the moment, big risk score, go check them out, here's what we know. And you'll be able to get that. Then we have other stuff. If they're saying, yes, I'm going to go do this, it will grab their commitments from a previous check-in and put them into this check-in: hey, did you actually do your commitments? Did you do what you said you would do? And then you'll be able to see their consistency with doing it. And the last part is, if they're struggling with something, we can actually get some resources put there for them. For example, if someone is consistently struggling to hit their protein intake, it might suggest, hey, they're really struggling with their protein intake, you have a resource here about these easy to grab protein options, why don't you send that over to them? Here's the message and the link. You click one button, it's sent off to them, good to go.

So that is quite a lot. That is all the pattern detection we've built in so far, and we're still building more stuff into the check-in systems every day. But if you have questions about your check-in and what kinds of things you would like to implement, and maybe you've seen something there where you're like, oh, could this work with X, Y and Z, drop me a message and we can have a chat. And if you want to go and build all this stuff for yourself, let me know. We'll run a full audit on your business, I'll show you every single part of it, what can be done, and then from there you'll be able to make a good decision.