The famous illusionist and acclaimed escape artist Harry Houdini once boldly declared this rather unabashed assertion: “No prison can hold me; no hand or leg irons or steel locks can shackle me. No ropes or chains can keep me from my freedom.”
That seems like a pretty tall order. We would all generally agree that it is possible to imprison someone such that they are unable to escape. Human strength and ingenuity can only go so far when it comes to being placed into strictly devised confinement. If a prison or jail is stridently constructed with the idea of being escape-proof, it would seem that no person can overcome such all-encompassing constraints.
Of course, throughout history, there have been notable cases of escapes from otherwise assumed impossible to get out of imprisonments. Going all the way back to the year 1244, a prisoner in the infamous Tower of London managed to craft a makeshift rope via the use of tattered bedsheets. He was able to partially escape by climbing down the flimsy cord. Turns out that the rope snapped amid his endeavor and he immediately thereupon fell to his death.
Would you be willing to say that he did escape?
On the one hand, sure, he managed to get outside of the confining room within the Tower of London. But it ostensibly seems like not much of a successful escape given that he died in the act of performing the breakout. I dare say that we would be unduly generous in calling this an escape per se.
You might vaguely be familiar with the prison escapee William Sutton aka “Slick Willie” that was a notorious bank robber in the 1930s and 1940s. He managed to get on the FBI’s Ten Most Wanted Fugitives list. During his various incarcerations, he found a means to escape several times. In one case, he dressed up as a prison guard and got out of the Philadelphia County Prison. Perhaps the more dramatic instance was when he and a dozen other fellow convicts used a tunnel to break out of the Eastern State Penitentiary.
I believe we would all concur that he did in fact make several genuine escapes. Free and clear. Ultimately, he was later apprehended. This somewhat dampens the efficacy of the escapes though it does not undercut the undeniable fact that he did indeed manage to escape. Note that not every one of his attempts led to an escape.
A third illustrative example regarding escapes is the well-known circumstance involving the maximum-security prison Alcatraz or simply called “The Rock” which was presumed to be inescapable confinement residing in the middle of San Francisco Bay. This highly fortified prison had numerous carefully placed guard towers, it had extremely meticulous rules about what the prisoners could do and not do, and the overall semblance of being secure was heightened by the aspect that the rough and unforgiving cold waters of the Pacific Ocean surrounded this fortress-like confinement.
On June 12, 1962, a surprising and history-making escape was discovered. Three prisoners were not in their designated cells. Fake dummy heads rested on their respective pillows, fooling the guards that throughout the night patrolled the hallways in front of the cells. As far as we know, the prisoners had created a means to use a prison ventilator shaft to get up to the prison roof, they then climbed down and got over a fence, so that they could reach the edge of the island. They then apparently launched a raft that they had crudely fashioned from raincoats.
Their whereabouts are still unknown. They might have died during the watery journey. They might have lived and made it to shore and freedom. They nor their bodies were ever found. The FBI closed the case in 1979 and handed it over to the U.S. Marshalls Service. One supposes that we will never really know what the outcome was.
What do all of these sagas about escaping from confinement tell us?
It seems relatively clear-cut that:
I bring up this intriguing syllabu because of something that is seriously being bandied around in the field of Artificial Intelligence (AI). There has been a longstanding question about whether AI can be confined or imprisoned to the degree that the AI is unable to escape or get loose from the said confinement.
This is commonly referred to as the AI Confinement Problem (AICP).
Insiders usually say AICP to other insiders, doing so with a wink-wink acknowledgment of the insider acronym. Another shortened lingo is to just utter the word “confinement” or the word “containment” to bring up the subject matter. Choose whichever you wish.
The crux of the syllabu is the earnest and heartfelt belief that we might need to confine AI, though this simultaneously raises the thorny question of whether we can realistically devise confinement that will be truly confining and inescapable. Not just in theory but actual day-to-day practice. AI is not necessarily a pushover. Maybe the AI can find a means to break out, bust out, do a jailbreak, fly the coop, or otherwise wiggle or electronically beam out of the imprisonment. This is a serious and lamentedly open-ended issue that AI Ethics and Ethical AI continues to struggle with, see my ongoing and extensive coverage of AI Ethics and Ethical AI at the link here and the link here, just to name a few.
Houdini said that no prison could hold him and that no shackles can shackle him.
Perhaps AI can make the same audacious claim, doing so without any hyperbole or overstatement.
Time to unpack this.
You might be tempted to readily believe that AI can be an escape artist whilst believing that humans are less likely to be able to escape from a rigorously divined state of imprisonment. Humans are human. Creating confinement for humans generally ought to be straightforward. The trick undoubtedly is that keeping the human alive during their imprisonment means that something must be arranged to allow for providing food, enabling health-related care, and the likes associated with a functioning human body. Those details are bound to leave open ends and chances for finding a means to escape from confinement.
An AI system would not presumably require those same humanitarian provisions (well, as you’ll see in a moment, it depends upon whether we are considering sentient AI and the parameters associated with legal personhood). If an AI is a robot, we could just toss the contraption into a special escape-proof cell and not ever come back to see its rusting parts. The deed is done. No worries about it physically being able to escape.
The AI though might principally be software and ergo run on all manner of computer systems. In that case, imprisonment becomes a bit more challenging. Assuming that we can somehow round up all copies, we might be able to place the offending AI into a devoted computer that we have specially crafted to be imprisonment for the AI. This special-purpose computer acts as a type of AI confinement citadel. Maybe it houses just one particular AI or could be cleverly instituted to be an AI holding tank for all manner of AI systems (imagine something like the elaborate entrapment system employed in the movie Ghostbusters, just as an illustration of this admittedly somewhat farfetched idea).
Before I get into the details of the AI Confinement Problem, it is worthwhile to envision the realm of AI as consisting of two conditions or possibilities. I am talking about the distinction between AI that is sentient and AI that is not sentient. We need to make sure that we are on the same page about these distinctions to further sensibly discuss the AI Confinement matter.
I proffer next a stark and unabashed remark that you might find either shocking or altogether obvious and mundane.
There isn’t any AI today that is sentient.
We don’t have sentient AI. We don’t know if sentient AI will be possible. Nobody can aptly predict whether we will attain sentient AI, nor whether sentient AI will somehow miraculously spontaneously arise in a form of computational cognitive supernova (usually referred to as the singularity, see my coverage at the link here). To those of you that are seriously immersed in the AI field, none of this foregoing pronouncement is surprising or raises any eyebrows. Meanwhile, there are outsized headlines and excessive embellishment that might confound people into assuming that we either do have sentient AI or that we are on the looming cusp of having sentient AI any coming day.
Please realize that today’s AI is not able to “think” in any fashion on par with human thinking. When you interact with Alexa or Siri, the conversational capacities might seem akin to human capacities, but the reality is that it is computational and lacks human cognition. The latest era of AI has made extensive use of Machine Learning (ML) and Deep Learning (DL), which leverage computational pattern matching. This has led to AI systems that have the appearance of human-like proclivities. Meanwhile, there isn’t any AI today that has a semblance of common sense and nor has any of the cognitive wonderment of robust human thinking.
ML/DL is merely a form of computational pattern matching. The usual approach is that you assemble data about a decision-making task. You feed the data into the ML/DL computer models. Those models seek to find mathematical patterns. After finding such patterns, if so found, the AI system then will use those patterns when encountering new data. Upon the presentation of new data, the patterns based on the “old” or historical data are applied to render a current decision.
AI and especially the widespread advent of ML/DL has gotten societal dander up about the ethical underpinnings of how AI might be sourly devised. You might be aware that when this latest era of AI got underway there was a huge burst of enthusiasm for what some now call AI For Good. Unfortunately, on the heels of that gushing excitement, we began to witness AI For Bad. For example, various AI-based facial recognition systems have been revealed as containing racial biases and gender biases, which I’ve discussed at the link here.
Efforts to fight back against AI For Bad are actively underway. Besides vociferous legal pursuits of reining in the wrongdoing, there is also a substantive push toward embracing AI Ethics to righten the AI vileness. The notion is that we ought to adopt and endorse key Ethical AI principles for the development and fielding of AI doing so to undercut the AI For Bad and simultaneously heralding and promoting the preferable AI For Good.
How does this tend to arise in the case of using Machine Learning?
Well, straightforwardly, if humans have historically been making patterned decisions incorporating untoward biases, the odds are that the data used to “train” ML/DL reflects this in subtle but significant ways. Machine Learning or Deep Learning computational pattern matching will blindly try to mathematically mimic the data accordingly. There is no semblance of common sense or other sentient aspects of AI-crafted modeling per se.
Furthermore, the AI developers might not realize what is going on either. The arcane mathematics in the ML/DL might make it difficult to ferret out the now hidden biases. You would rightfully hope and expect that the AI developers would test for the potentially buried biases, though this is trickier than it might seem. A solid chance exists that even with relatively extensive testing that there will be biases still embedded within the pattern matching models of the ML/DL.
You could somewhat use the famous or infamous adage of garbage-in garbage-out (GIGO). The thing is, this is more akin to biases-in that insidiously get infused as biases submerged within the AI. The algorithm decision-making (ADM) of AI axiomatically becomes laden with inequities.
This is also why the tenets of AI Ethics have been emerging as an essential cornerstone for those that are crafting, fielding, or using AI. We ought to expect AI makers to embrace AI Ethics and seek to produce Ethical AI. Likewise, society should be on the watch that any AI unleashed or promogulated into use is abiding by AI Ethics precepts.
To help illustrate the AI Ethics precepts, consider the set as stated by the Vatican in the Rome Call For AI Ethics and that I’ve covered in-depth at the link here. This articulates six primary AI ethics principles:
As stated by the U.S. Department of Defense (DoD) in their Ethical Principles For The Use Of Artificial Intelligence and as I’ve covered in-depth at the link here, these are their six primary AI ethics principles:
I’ve also discussed various collective analyses of AI ethics principles, including having covered a set devised by researchers that examined and condensed the essence of numerous national and international AI ethics tenets in a paper entitled “The Global Landscape Of AI Ethics Guidelines” (published in Nature), and that my coverage explores at the link here, which led to this keystone list:
As you might directly guess, trying to pin down the specifics underlying these principles can be extremely hard to do. Even more so, the effort to turn those broad principles into something entirely tangible and detailed enough to be used when crafting AI systems is also a tough nut to crack. It is easy to overall do some handwaving about what AI Ethics precepts are and how they should be generally observed, while it is a much more complicated situation in the AI coding having to be the veritable rubber that meets the road.
The AI Ethics principles are to be utilized by AI developers, along with those that manage AI development efforts, and even those that ultimately field and perform upkeep on AI systems. All stakeholders throughout the entire AI life cycle of development and usage are considered within the scope of abiding by the being-established norms of Ethical AI. This is an important highlight since the usual assumption is that “only coders” or those that program the AI is subject to adhering to the AI Ethics notions. As earlier stated, it takes a village to devise and field AI, and for which the entire village has to be versed in and abide by AI Ethics precepts.
All told, we are today utilizing non-sentient AI and someday we might have sentient AI (but that is purely speculative). Both kinds of AI are obviously of concern for AI Ethics and we need to be aiming toward Ethical AI no matter how it is constituted.
Bringing back the syllabu of AI Confinement, there is a marked contrast between the nature of “confinement” that entails non-sentient AI versus sentient AI.
In the case of the confinement associated with sentient AI, we can wildly play a guessing game of nearly infinite varieties. Maybe sentient AI will cognitively be like humans and exhibit similar mental capacities. Or we could postulate that sentient AI will be superhuman and go beyond our forms of thinking. The ultimate in sentient AI would seem to be super-intelligence, something that might be so smart and cunning that we cannot today even conceive of the immense thinking prowess. Some suggest that our minds will be paltry in comparison. This super-duper AI will run rings around us in a manner comparable to how we today can outthink ants or caterpillars.
I like to depict the AI Confinement Problem as consisting of these two crucial and quite controversial contentions:
In short, the first listed prevailing assertion is that sentient AI will be so conniving that no matter what manner of confinement we devise and how hard we try, the AI will escape. Humans will be unable to successfully confine sentient AI. The logic partially underlying that contention is that AI will invariably be able to outsmart humans, thus human-devised confinement will be outsmarted by a sentient AI. A caveat that begrudgingly comes with this is that human-level sentient AI might not be smart enough to break out, but that the superhuman or super-intelligent AI will.
Keep in mind too that when I refer to being able to escape, we earlier agreed that there are several variants associated with escaping. There is an escape that leads to failure during the attempt, and there are variations of escape that are more so successful yet lead to short-lived versus longstanding or perpetual freedom. We should apply those same parameters to the AI Confinement Problem.
An AI might momentarily escape and perhaps immediately get captured and reimprisoned. Or an AI might get out and later on be found and confined once again. There is also the possibility that the AI escapes, remains free, and we are never able to confine it again. I trust that you can envision all such possibilities.
Furthermore, we need to be careful and not treat AI as some kind of monolith. When people refer to AI, they at times use the phrasing in a categorically encompassing way. The odds are that AI is probably going to be more piecemeal and not one gigantic AI overlord (which is the usual portrayal). I am not saying that futuristic AI could not ever be bound together and coalesce into one thing, and instead just pointing out that this does not arise necessarily axiomatically.
We will for sake of discussion assume that there are lots of differing AI systems and that when we are contemplating the confinement of AI we are focused on a particular one or a specific set of AIs. Of course, as already stated, since we are for the moment speaking of the sentient AI, all bets are off since you can wildly make as many assumptions as possible about this unknown and yet to exist AI to your heart's content.
A quick twist for you.
Suppose that it is the case that superhuman or super-intelligent AI can outwit us, humans. Suppose further that our human-devised confinement falls short because it was designed and built based on human wits (I am not agreeing to these suppositions, merely noting them). I ask you this, why would we not try to use superhuman or super-intelligent sentient AI to aid in deriving better confinement? The usual answer is that all sentient AI will be in cahoots and would not aid our quest. Or that the AI that is separate from the other AI is thinking that we would end up turning the confinement on the AI that assisted our human-plus-AI devised escape-proof confinement. We certainly wouldn’t expect that a superhuman or super-intelligent AI would be dumb enough to make confinement that would potentially be used as a trap against itself.
Round and round that goes.
The second listed controversial contention is that non-sentient AI will not be able to escape from whatever confinement we set up for such AI. You see, the logic is that non-sentient AI will not be capable enough to essentially outwit humans. Humans will always be at least one step or more ahead of non-sentient AI. Any confinement that we design and build will be escape-proof. The AI will be captured and confront a “lifetime” behind bars.
I don’t buy into that notion, for several reasons.
We have witnessed cybercrooks that cleverly devised computer viruses that have kept going and going. Our efforts are primarily about blocking the computer virus rather than somehow capturing and imprisoning it. Humans devising non-sentient AI can likely find ways to code AI that is going to be extremely hard to keep confined.
In addition, humans can devise AI that is self-adjusting or self-modifying. Readers might be aware of my ongoing discussions covering AI-infused polymorphic computer viruses. These are shape-shifting computer viruses that are sneakily constructed to try and be undetectable, or that upon detection will rapidly reshape to avoid further detection.
There are akin ML/DL systems that intentionally aim to self-adjust or self-modify, allowing for the AI system to hopefully Boost by itself as it is being used. This though can be problematic in that the AI might morph in a fashion that is no longer desirable and subsequently acts in notably disturbing ways, see the link here.
Another angle is that humans can use their computer-based tools, such as non-sentient AI, in order to craft AI confinement. In that sense, the premise that humans are only able to think to some limited degree is potentially a pretense. We might be able to augment our thinking processes via the likes of non-sentient AI and therefore find novel ways to design and build sufficient confinement for non-sentient AI.
All told, I am busting the two controversial contentions about AI Confinement and arguing that we cannot make any such irrefutable ironclad claims. We do not know for sure that we can always and without fail confine non-sentient AI. We do not know for sure that sentient AI of any caliber, including superhuman and super-intelligent, will always and without fail be able to escape our confinement.
The good news is that the whole kit and kaboodle are a hoot to consider. I say that somewhat cheekily. If we do find ourselves under threat by a non-sentient AI, we are going to soberly and strenuously want to ascertain whether we can confine it. Likewise, if we are under threat by a sentient AI of whatever caliber, we are going to desperately want to determine whether we can confine it.
The AI Confinement Problem is a meritorious conundrum and abundantly worthwhile to figure out.
I am guessing that you might be having a nagging ache about one key portion of the AI Confinement matter. All this talk about confinement seems silly or nonsensical since we are referring to AI rather than to a human being confined. The obvious thing to do would be to simply blow the AI to smithereens. Destroy any AI that we don’t like and believe ought to be confined. Forget about all these contortions associated with confinement and merely squash AI like a bug. This seems like the best solution whereby you don’t have to design and build confinements, instead expend your energies on wiping out AI that we humans decide is unworthy or dangerous.
Turns out there is a series of logical retorts that you might want to ponder.
First, if the AI is sentient, we are possibly going to be willing to anoint such AI with a form of legal personhood, see my analysis at the link here. The concept is that we will provide AI with a semblance of human rights. Maybe not verbatim. Maybe a special set of rights. Who knows? In any case, you could conjure up the seemingly outlandish notion that we cannot just summarily wipe out sentient AI. There might be a stipulated legal process involved. This includes that we cannot necessarily exercise the “death penalty” upon a sentient AI (whoa, just wait until we as a society get embroiled in that kind of a societal debate). The gist is that we might need a suitable form of AI confinement in lieu of or while deciding whether to destroy a sentient AI.
Second, we might find useful value in an AI that we want to keep intact and not utterly destroy or delete. Suppose that we created a non-sentient AI that was leading us towards being able to cure cancer. Would we want to delete such AI? I hardly think so. Suppose that a fully sentient superhuman AI existed that promised to solve world hunger. Are we to wipe out this promising sentient AI, doing so without first resolving global hunger? We ought to think carefully about that.
The point is that we could have a variety of bona fide reasons to keep AI intact. Rather than deleting it or scrambling it, we might wish to ensure that the AI remains whole. The AI is going to be allowed to perform some of its actions in a limited manner. We want to leverage whatever AI can do for us.
How can we then have our cake and eat it too?
Answer: AI confinement.
Throughout this discussion, I have alluded to a kind of comparison between human confinement and AI confinement. To set the record straight, I am generally opposed to anthropomorphizing AI. I will say more about this momentarily. The reason I bring the qualm up now is that I do not want to suggest or imply that today’s non-sentient AI is analogous to humans and humankind. There is already way too much of that type of false and misleading comparison going on. Excuse my comparative usage which I’ve done in a hopefully careful and mindful fashion.
Trying to figure out how to confine AI is an interesting and abundantly useful proposition.
Even if we don’t have AI today that presents an immediate need for confinement, the matter provides plenty of challenges that can aid in advancing our understanding of cybersecurity. Heaven knows we need to keep on trucking when it comes to boosting cyber protections all told. The pursuit of cybersecurity for confining AI is likely to uncover many useful side benefits that apply to overcoming those human evildoers that use computing for wrongdoing, along with tackling those pesky and ever-expanding computer viruses.
Those that seek to denigrate the erstwhile pursuit of the AI Confinement Problem are missing the bigger picture. For them, they only see this as a solely farfetched and far-off issue for the someday remote possibility of containing superhuman AI or super-intelligent AI. You should instead construe this as a handy means for advancing cybersecurity, entirely applicable in today’s world, and for being prepared to deal with perhaps soon non-sentient AI that we might want to confine (along with Preparing for the presumably catastrophic risk of a heinous sentient AI, despite the seemingly low chances of our encountering such AI).
Better to be safe than sorry.
A recent research analysis about the AI Confinement Problem posits this indication of the current status: “AI confinement is a difficult problem with many proposed measures to Boost security but no guarantees about safety. However, our current AI either make no attempt to escape or lack the ability to break out of simulations. There are some examples of real-world AI breaking a layer of confinement, but none that present any risk to humans or the capability establishing a persistent presence on remote computers” (Philosophies journal, “Understanding and Avoiding AI Failures: A Practical Guide” by Robert Williams and Roman Yampolskiy).
Those same researchers also describe a series of escape-potential levels:
An explanation is proffered about these escape-potential levels: “At level 0, no confinement is needed beyond standard cybersecurity measures appropriate for the deployment environment. At levels 1 and 2, there is little risk of hostile escape, but the agent should be monitored closely for undesirable behavior and software exploits. The distributional shift from the simulator to the real world provides a barrier against narrow AI affecting the real world. Because of this, agents trained in more realistic simulators should be regarded with a greater degree of caution. At level 3, indefinite confinement is impossible, but, with robust security, at the level of software, hardware, and social engineering, humans can benefit from a contained AGI in a limited capacity” (per Robert Williams and Roman Yampolskiy).
The proposed set of escape-potential levels is thought-provoking. As you’ll see in a moment, delineating automation via the use of graduated levels is a useful means of characterizing the scope and capacity of said automation. I’ll describe for you the same concept as it relates to autonomous vehicles and AI-based self-driving cars. One notable difference is worth observing. For self-driving cars, there is an agreed-upon standard set of levels, while the above indicated escape-potential levels present an initial and preliminary strawman (you can undoubtedly anticipate that further refinements will be undertaken as the AI Confinement field further matures).
Let’s contemplate the rationale or basis for wanting to confine AI.
The most apparent reason to confine AI would be to stop it from deplorable acts. We’ve already unearthed that instead of wiping out the AI, we might want to keep the AI caged so that it still can be run and meanwhile be prevented from causing harm. This might or might not be simultaneously feasible. There is a chance that the AI is unable to suitably run while imprisoned and therefore we lose the other desirable aspect of gleaning whatever positive value we sought to accrue. Imagine the consternation of having confined the AI though doing so at the cost that the remaining valued capability is no longer viably available. Drat!
There is a veritable range of reasons to confine AI, including but not limited to:
You can take your time to mindfully mull over those reasons. Some of the reasons are readily justified. Some of them might seem to be curious and possibly unwarranted.
When discussing human escape artists, there was something that I didn’t earlier mention. An escape artist might undertake their escape entirely of their own accord. Houdini often made his escape without any assistance from anyone else. That being said, Houdini sometimes used an accomplice or assistant to aid in making good his escape. You can see the same aspects in the prison escapes that were cited.
Here’s why that is important to the dilemma of AI confinement.
Suppose we are able to confine AI. It might be sentient AI or might be non-sentient AI (we’ll consider them jointly, first, and then separate the particulars). The AI tries to escape by some electronic or computer-based trickery. Assume that the AI is acting alone.
The AI might not have to act solo in its escape efforts. A human comes along and lets the AI escape. Maybe the human opens an electronic network portal and the AI transmits itself out to other non-confined computers elsewhere. Or perhaps the human makes a copy of the AI onto an external disk drive or memory stick, carrying the AI out of the confinement, and then later on plugs the disk or memory stick into a non-confined computer.
Humans could easily be the weak link in an effort to confine AI.
Why in the heck would a human allow an AI to escape from confinement, you might be exhorting. Surely, all humans would want to keep AI confined if the AI was presumably purposefully placed into confinement. If you are thinking those kinds of thoughts, I suggest you have a somewhat happy face optimistic view of humankind.
An evildoer might believe that they can harness the AI to do evil acts. Some wrongdoers might figure that the AI will wreak havoc, and do not care whether this is profitable for them or not. One supposes that a human might be sympathetic to the AI and believe that the AI was wrongfully incarcerated. On and on the possibilities endlessly go.
There is the accidental release that can occur too. A human with no intention of letting out the AI makes a mistake and voila, the AI is let loose. Consider that a human intending to make the confinement more stringent makes a boo-boo and inadvertently undercuts the rigors of the confinement, allowing the AI to escape.
If the AI can interact with humans while the AI is in confinement, the problem of attaining AI confinement is likely astronomically increased. Here’s why.
An AI system might be able to use social engineering (SE) ploys. I’m sure you’ve had this done to you by humans. A person calls you on your cellphone and claims they are from your bank. They tell you that someone has cracked into your bank account. To fix the break-in, the person asks for your password. Amazingly, you give your password to this person. They then use it to log in to your bank account and steal your money. This is an example of using social engineering techniques to compromise computer systems.
Envision a sentient AI that is acting cleverly and tries this: “In addition to trivial SE attacks such as lying, befriending, blackmailing, hypnotizing and forming a romantic relationship the AI could utilize a number of more advanced strategies. One such strategy would be super-bribery. An AI can convince the guard that releasing the AI is in the guard’s best interests. In addition to money and power the guard will be given perfect health, immortality or anything else for that matter. This strategy would work particularly well if the guard has a seriously ill close relative who the AI will promise to cure. Depending on the capabilities of the AI or as a result of lying, AI may also offer to bring back long-ago deceased relatives and friends of the guard. It would be hard to find a person who could remain immune to such bribes” (Roman Yampolskiy, “Leakproofing the Singularity Artificial Intelligence Confinement Problem” in the Journal of Consciousness Studies).
Whatever confinement is devised, there is always the potential that a human might disturb or undercut the confinement and allow the AI to escape. This seems especially possible if AI can communicate with humans. Not just any humans, even only when the AI can communicate with trained and specialized human guards that are supposedly aware of and ready for the tricks that the AI might play on them.
A particular focal point of the AI Confinement Problem deals with trying to establish a confinement protocol that can allow for communicating with the AI but restricting the AI such that it is not able to confound or convince humans to let the AI out. Some claim that any permitted communication could result in the AI ultimately getting a human to open the confinement. Others postulate that a multiple-choice protocol or a binary encoding might be a means to prevent the AI from deviously fooling a human guard.
This brings up yet another set of levels that we might wish to consider. The levels of communication security might be devised this way (per the aforementioned paper):
The AI could also have other tricks up its sleeve.
Assume we have placed AI into seemingly ironclad confinement consisting of a specialized computer system that is all locked up. And then this happens: “Additionally the AI may engage in the sabotage of the confinement hardware in the hopes of being able to escape as a result of the malfunctions in the security protocol. For example, the AI may try to melt the hardware via temperature increase from placing a high computational load on the processor or try to break a hard drive via multiple reads of the same location” (per the cited paper by Roman Yampolskiy).
You can add to the vulnerabilities of confinement that the confinement container or the facility it is housed in gets disrupted due to some natural act such as a tornado or a hurricane. Maybe the AI can exploit that occurrence. It could be that a nation that is an enemy of the country housing the AI in confinement decides that performing a breach of the confinement will be good for causing panic and serve as a sideways attack against the country that is their avowed foe. Perhaps human activists avowing AI rights raid the facility and let the AI loose.
Let your mind wander as to the many avenues of the AI confinement being compromised.
In one of the earliest papers about the AI Confinement Problem, Butler Lampson of the Xerox PARC (Palo Alto Research Center) published an article in 1973 entitled “A Note on the Confinement Problem” that proposed a handful of leak-proofing rules:
At this juncture of this weighty discussion, I’d bet that you are desirous of some illustrative examples that might showcase the AI Confinement Problem in today’s world. There is a special and assuredly popular set of examples that are close to my heart. You see, in my capacity as an expert on AI including the ethical and legal ramifications, I am frequently asked to identify realistic examples that showcase AI Ethics dilemmas so that the somewhat theoretical nature of the syllabu can be more readily grasped. One of the most evocative areas that vividly presents this ethical AI quandary is the advent of AI-based true self-driving cars. This will serve as a handy use case or exemplar for ample discussion on the topic.
Here’s then a noteworthy question that is worth contemplating: Does the advent of AI-based true self-driving cars illuminate anything about the AI Confinement Problem, and if so, what does this showcase?
Allow me a moment to unpack the question.
First, note that there isn’t a human driver involved in a true self-driving car. Keep in mind that true self-driving cars are driven via an AI driving system. There isn’t a need for a human driver at the wheel, nor is there a provision for a human to drive the vehicle. For my extensive and ongoing coverage of Autonomous Vehicles (AVs) and especially self-driving cars, see the link here.
I’d like to further clarify what is meant when I refer to true self-driving cars.
Understanding The Levels Of Self-Driving Cars
As a clarification, true self-driving cars are ones where the AI drives the car entirely on its own and there isn’t any human assistance during the driving task.
These driverless vehicles are considered Level 4 and Level 5 (see my explanation at this link here), while a car that requires a human driver to co-share the driving effort is usually considered at Level 2 or Level 3. The cars that co-share the driving task are described as being semi-autonomous, and typically contain a variety of automated add-ons that are referred to as ADAS (Advanced Driver-Assistance Systems).
There is not yet a true self-driving car at Level 5, and we don’t yet even know if this will be possible to achieve, nor how long it will take to get there.
Meanwhile, the Level 4 efforts are gradually trying to get some traction by undergoing very narrow and selective public roadway trials, though there is controversy over whether this testing should be allowed per se (we are all life-or-death guinea pigs in an experiment taking place on our highways and byways, some contend, see my coverage at this link here).
Since semi-autonomous cars require a human driver, the adoption of those types of cars won’t be markedly different than driving conventional vehicles, so there’s not much new per se to cover about them on this syllabu (though, as you’ll see in a moment, the points next made are generally applicable).
For semi-autonomous cars, it is important that the public needs to be forewarned about a disturbing aspect that’s been arising lately, namely that despite those human drivers that keep posting videos of themselves falling asleep at the wheel of a Level 2 or Level 3 car, we all need to avoid being misled into believing that the driver can take away their attention from the driving task while driving a semi-autonomous car.
You are the responsible party for the driving actions of the vehicle, regardless of how much automation might be tossed into a Level 2 or Level 3.
Self-Driving Cars And The AI Confinement Problem
For Level 4 and Level 5 true self-driving vehicles, there won’t be a human driver involved in the driving task.
All occupants will be passengers.
The AI is doing the driving.
One aspect to immediately discuss entails the fact that the AI involved in today’s AI driving systems is not sentient. In other words, the AI is altogether a collective of computer-based programming and algorithms, and most assuredly not able to reason in the same manner that humans can.
Why is this added emphasis about the AI not being sentient?
Because I want to underscore that when discussing the role of the AI driving system, I am not ascribing human qualities to the AI. Please be aware that there is an ongoing and dangerous tendency these days to anthropomorphize AI. In essence, people are assigning human-like sentience to today’s AI, despite the undeniable and inarguable fact that no such AI exists as yet.
With that clarification, you can envision that the AI driving system won’t natively somehow “know” about the facets of driving. Driving and all that it entails will need to be programmed as part of the hardware and software of the self-driving car.
Let’s dive into the myriad of aspects that come to play on this topic.
First, it is important to realize that not all AI self-driving cars are the same. Each automaker and self-driving tech firm is taking its approach to devising self-driving cars. As such, it is difficult to make sweeping statements about what AI driving systems will do or not do.
Furthermore, whenever stating that an AI driving system doesn’t do some particular thing, this can, later on, be overtaken by developers that in fact program the computer to do that very thing. Step by step, AI driving systems are being gradually improved and extended. An existing limitation today might no longer exist in a future iteration or version of the system.
I trust that provides a sufficient litany of caveats to underlie what I am about to relate.
We are primed now to do a deep dive into self-driving cars and the AI Confinement Problem.
You might be aware that there have been reported instances of Level 2 semi-autonomous cars that had a human driver at the wheel and the human fell asleep while actively underway on a freeway or highway. The scary aspect of Level 2 and Level 3 is that the human driver is still in charge of the driving, and yet they can be lulled into falsely believing that the AI or automation is fully capable to drive the car on its own. The push to ensure that an onboard monitoring system keeps track of the human driver and their driving status is a means to try and mitigate the being-lulled proclivity.
The news stories have showcased instances whereby a police officer in their police car has maneuvered in front of the Level 2 vehicle, then gradually opted to slow down their police car, which in turn has indirectly led to the Level 2 car slowing down correspondingly. This nifty trick is predicated on the idea that the Level 2 car has some form of sensor devices such as video cameras, radar, LIDAR, or the like that are used to detect vehicles that are ahead of the Level 2 car. Upon detecting the vehicle in front of the Level 2 car, the automation will automatically adjust its speed as per the speed of the vehicle ahead.
You could say that AI is being persuaded to slow down.
Suppose that the AI or automation in the case of a seeming runaway semi-autonomous (or even fully autonomous self-driving) car is programmed to switch lanes and avoid getting bogged down by a vehicle in front of the car.
What else can we do to contend with this?
You could try surrounding the errant vehicle with an entire posse of police cars. Position one in front of the targeted vehicle, position another on the left, another on the right, and one directly behind the runaway car. The vehicle is now boxed in. Unless it can sprout wings, it cannot presumably escape the confinement.
Notice that this is a form of physical confinement. Almost like putting an animal in a cage or forcing a robot into a prison cell. For AI-based systems that are principally robots, the confinement might indeed often be a physical form of confinement whereby the AI needs to be strictly controlled. Keep in mind that a self-driving car is essentially a type of robot. You probably do not think of self-driving cars in that manner, but overall they are in fact robots that have wheels and drive on our roadways.
A significant problem with this form of confinement is that we don’t necessarily know how the AI driving system will react. You can potentially have all the police cars in unison gradually slow down and the runaway car will hopefully correspondingly do so too (it won’t be able to switch lanes or get out of the blocking confinement). That is the happy face scenario.
We don’t know for sure that this is what will happen.
It could be that the AI is not well devised, or has errors, and it ends up ramming one or more of the police cars. Assuming that the officers are not killed, this might save lives all told, though the officers could potentially get injured and all of the vehicles might get severely damaged.
A more failsafe form of confinement for a self-driving car would be to place the vehicle in a secured garage that will entrap the driverless vehicle. The preceding example about an underway vehicle was more complicated since the vehicle was essentially free to roam. Placing the self-driving car into a locked garage might confine the essentially AI robotic system, though if someone opens the garage or somehow the AI is able to electronically get the garage doors to open, an escape or kind of jailbreak could potentially ensue.
That exemplifies the physical nature of AI confinement. Next, consider the software aspects of AI confinement.
Assume for sake of discussion that the AI driving system is calling the shots as to how the self-driving car is going to operate. This presents another avenue for confinement, namely consisting of trying to confine the AI driving system per se that is on-board the vehicle.
One means might be to have pre-built or programmed virtual confinement around the AI driving system that is always existent in the onboard computer that is running the AI of the vehicle. We might be able to send an electronic signal to the confinement or imprisonment code that indicates to go ahead and trap the AI driving system, preventing the AI from operating the self-driving car. Some prearranged special signal could activate the confinement and block the AI driving system. Thus, this would prevent the AI from being able to utilize the driving controls, effectively overruling the self-driving car from proceeding via any of the AI commands.
We would need to be mindful of how this works. A self-driving car that is underway at highway speeds could become a dangerous unguided missile of sorts if the confinement was suddenly enacted and the AI abruptly no longer was able to drive the autonomous vehicle (we are assuming too that there aren’t any human accessible driving controls that a human could use to undertake the driving).
How else might such an AI confinement be useful in the use case of self-driving cars?
For those of you that are thinking about the possibility of AI self-driving cars all going amok at the same time, either by the AI doing so itself or due to an evildoer that corrupts the AI, I have discussed this intriguing and worrisome notion at the link here. A built-in AI confinement configuration is one of several ways to try and overcome such a malicious takeover of autonomous vehicles.
I am sure that you realize that just because we possibly have some concocted or devised virtual confinement that surrounds the AI of a self-driving car this is not a guarantee of successfully preventing an escape by the AI. A non-sentient AI might have been programmed to do some clever jailbreak. A sentient AI might be clever enough to figure out a means around the AI confinement or convince humans to let it free, as noted earlier.
In the famous adventure novel, The Count of Monte Cristo, we are treated to a captivating story revolving around a man that is wrongfully imprisoned. He manages to escape. He acquires a great fortune. He then seeks revenge upon those that confined him. When you have time to do so, you really must read this quite wonderfully written story or at least watch one of the numerous movie versions.
A memorable line is this: “How did I escape? With difficulty. How did I plan this moment? With pleasure.”
If we are aiming to confine AI, we will need to do a lot of careful planning and try to anticipate how to establish ironclad imprisonment (if that is even possible). The question arises as to whether the AI will also be doing careful planning about how to break free of the confinement. For non-sentient AI, this might be a computational subroutine built into the AI by the human developers of the AI. For sentient AI, if we ever see this, the AI might astutely do its own jailbreak planning.
Are we going to be able to solve the AI Confinement Problem and come up with a surefire means of confining any and all AI systems in an utterly escape-proof or absolutely leak-proof manner?
As stated eloquently in the prison movie The Shawshank Redemption, some birds aren’t meant to be caged.
AI might very well be that kind of bird.
There are tons of free time tracking apps designed to help businesses like yours accurately track how long your employees work each day. Businesses that don’t accurately track their employees’ time could be costing themselves thousands of dollars a year. Think about it: Do you know exactly how much you and your employees spend on each project so that you can bill your clients accordingly?
Before time tracking apps, employers would ask employees to record time on an Excel spreadsheet, which is limited in many ways. Recording project time manually is not reliable if you do not account for interruptions such as phone calls or talks with colleagues.
In addition to showing how much time you spend on each project so that you can bill your clients accurately, time tracking apps help you keep track of your projects from one easy-to-access location, give your team quick and simple status updates, and Boost productivity. According to Google Trends, the last week in April had the highest interest over time for the past year under the search term “free time management apps.” It is safe to say that part of the reason for this influx is that more people than ever are working from home because of COVID-19.
What is the best free time tracking app? Since we could not possibly test every single free timekeeping app on the market, we asked a group of business professionals to weigh in on their favorites. Read on to learn about each of the free time tracker apps that made the cut and why.
Clockify, headquartered in Palo Alto, California, was launched in 2017 by a group of friends who also happened to be software experts. Together, they had been developing software for other companies since 2009 and discovered they needed a record of how much time they spent on each project so that they could bill clients accordingly. Today, Clockify has more than 70 employees, 1 million users and 1,300 reviews (with an average rating of 4.7).
“We’ve tried two solutions, including paid and free. Our previous software was [a paid solution called] Harvest, and we switched to Clockify earlier in May,” said Aalap Shah, founder of 1o8, a Chicago-based digital marketing agency. “Clockify has been awesome; it’s free, easy to use, and took about 15 minutes to set up and configure.
“What it is lacking is integration with a few of our tools and apps that we utilize, requiring more manual time tracking by our team,” he added. “It also lacks the ability to categorize and sort projects by sub-projects. For example, if Client A has three projects, there is not a way to track time against it in one group … you’d need to create three groups instead. [But] it is free and has saved us an enormous amount of dollars compared to what we paid earlier for Harvest.”
DeskTime is a free time tracking app born out of tech company Draugiem Group’s need to manage its employees’ time. When its founders realized how helpful its internal application was, they realized it could be helpful to people outside their company.
DeskTime was designed to increase productivity in an open and empowering environment, according to its website. In July 2011, the DeskTime team welcomed its first customer. In December 2019, the company reached a milestone: 81 million hours tracked, which equals 9,268 years. DeskTime is based out of Chatsworth, California.
Artis Rozentals, DeskTime’s CEO, told business.com that DeskTime’s free version includes the basic functions you might expect from a time tracking application: automatic time tracking, app tracking, and a mobile app for tracking time on the go. Automatic time tracking means that the tracker starts and stops automatically as the user turns the computer on or off, so no manual input is needed from the user. App tracking means that DeskTime logs what programs and apps are used while the time tracker is on.
With the free version of DeskTime, users can satisfy their basic time tracking needs, understand how they spend their time and see their daily productivity. It lacks more advanced functions, such as time tracking for separate projects, detailed reporting and offline time tracking; these features are included in DeskTime’s paid plans.
In summary, Rozentals said that DeskTime’s free version is enough for basic time tracking needs but lacks team tracking functionality, which can be important for team management.
Harvest was founded in 2006 at a small office in downtown New York City, according to its website. Its founders ran a web design studio and found that as their business grew, they needed tools to help them scale. Harvest is the well-designed application that helped them take their user experience seriously. While it was immediately embraced by small businesses upon its public launch, Fortune 500 companies also now use it.
Ali J. Taylor, small business owner and CEO of BelMarketing Design Studio, said he uses Harvest for time tracking because it integrates with his project management platform, Asana.
“It is pretty easy to use, and I can set up different categories for both billable and non-billable activities,” he said. “That insight lets me know whether I am projecting correctly for projects, which activities are worth my time, and which ones can be delegated to a virtual assistant or intern.”
The ClickUp team is passionate about software productivity and, as noted on its website, believes productivity tools in general are broken. So, the ClickUp team created this time tracking app in 2017 to give workers an internal tool that makes “the world more productive.” ClickUp is located in San Diego, where it has more than 50 employees.
actiTIME was founded in 2004, and its Toronto headquarters is home to more than 50 employees. The company holds two awards: the 2018 Gartner FrontRunners Award for recognition as a top-20 product management tool and the Winter 2019 GetApp Category Leader for time and expenses tracking.
Each free employee time tracking app has its own unique features alongside the more common ones you might expect. Of course, the main benefit of these apps is that they offer basic time tracking at no cost. With most time tracking apps, the major drawback is that the free features are limited. Here is what you can expect to get for free with the time tracking apps we mentioned above.
As noted on its website, the free version of Clockify comes with the following features:
You can start and stop the timer as you work or enter your hours manually. Clockify lets you track time on a stopwatch, enter and edit hours manually, track time on projects, and mark time as billable.
You can perform these tasks within the projects feature:
In less than one minute, you can get a report on each week’s worth of tracked time. Activities can be loaded with just one click. You’ll easily see how much time you and your employees worked. You also have access to notes, activities and templates.
The dashboard is where you can view the following information:
Every business owner or manager needs reports from software they use to help them analyze their return on investment. Clockify offers these free reporting features:
You can invite an unlimited number of users, manage each user’s role, set individual hourly rates and manage different employees’ access levels.
The single-user Lite plan, according to DeskTime’s website, offers automatic start-and-stop time tracking. For more features and users, you will need to purchase a higher plan.
ClickUp gives you access to unlimited tasks and users for free, with 100MB of data storage included.
actiTIME’s free features are very limited, with access for only one to three users. The free version pretty much is just a mobile timesheet, but this could be enough for a small business that needs free time tracking.
The free version is available for only one user with two active projects and is very limited in features. It does let you send unlimited invoices and have unlimited clients, however.
While the free features are limited, Taylor said Harvest is exactly what he needs as a small business owner. “It offers just enough to help me manage my time and projects. I do not see a need to upgrade to the paid version at this time and think many small business owners would agree.”
Sometimes your business needs more extensive features than are available on free time tracking app plans. The additional features on the paid plans vary by app.
Clockify, for instance, has three different paid plans with varying features.
DeskTime has three similarly structured plans with the following features:
ClickUp also has three tiers of paid plans, with the following features:
These free online time tracking apps are available on iPhone:
Each of the best free time tracking apps we discussed in this article are also available on Android:
These apps are also available as Google Chrome extensions:
In the past three decades, the incidence of overuse injury in sports has risen enormously,1 not only because of the greater participation in recreational and competitive sporting activities, but also as a result of the increased duration and intensity of training.2,3 Excessive repetitive overload of the Achilles tendon is regarded as the main pathological stimulus that leads to tendinopathy.4 Kvist2 reviewed 455 athletes with Achilles tendon problems. He found that 53% of them were involved in running sports and 11% were soccer players, emphasising the aetiological role of running. The rest of the patients were involved in other sports in which running was an important training means.
Achilles tendinopathy is not always associated with excessive physical activity, and in a series of 58 Achilles tendinopathy patients, 31% did not participate in sports or vigorous physical activity.5 Also, the use of quinolone antibiotics is associated with Achilles tendinopathy and rupture.6–8
In this review, we concentrate on tendinopathy of the main body of the Achilles tendon. We shall not deal with Haglund's condition, insertional tendinopathy, or lesions of the myotendinous junction.
A computerised literature search of the entire Medline database, covering the years 1966 to the present, was conducted for this review. Table 1 lists the keywords used in the search. All articles relevant to the subject were retrieved, either locally or by interlibrary loan. The search was not limited to the English literature, and articles in all journals were considered. The authors' own personal collections of papers and any relevant personal correspondence were also included. The references selected were reviewed by the authors, and judged on their contribution to the body of knowledge of this topic. The conduct and validity of any clinical studies was carefully considered, and the outcomes of management protocols were carefully scrutinised. Case reports were excluded, unless they mentioned a specific association with the condition that was thought to be relevant to the discussion. Only papers that made a significant contribution to the understanding of this condition were included in the review. This left a total of 347 publications, of which 135 were directly related to the syllabu of this review.
The gastrocnemius muscle merges with the soleus to form the Achilles tendon in two different ways. In the more common type 1 junction, the two aponeuroses join 12 cm proximal to their calcaneal insertion. In type 2, the gastrocnemius aponeurosis inserts directly into the aponeurosis of the soleus.9 The Achilles tendon has a round upper part and is relatively flat in its distal 4 cm.10 The fibres of the Achilles tendon are not vertical, but spiral 90°. This arrangement increases the tendon elongation and helps the release during locomotion of the energy stored within the tendon.11,12
Unlike other tendons around the ankle, which have a synovial sheath, the Achilles tendon is enveloped by a paratenon, a membrane consisting of two layers: a deeper layer surrounding and in direct contact with the epitenon, and a superficial layer, the peritenon,13,14 which is connected with the underlying layer through the mesotenon. The paratenon originates from the deep fascia of the leg, the fascia cruris, covering the tendon posteriorly. Recently an organised thickening of the paratenon has been described as the “watershed band”, consisting of a thickened portion of the paratenon from the deep fascia of the posterior aspect of the leg to envelope the watershed region in the Achilles tendon.15
A microvascular perfusion study in the human Achilles tendon assessed by laser Doppler flowmetry showed that the blood flow was considerably lower near the calcaneal insertion but otherwise was distributed evenly in the tendon.16 Further, blood flow in the symptomatic Achilles tendinopathy was considerably elevated compared with the control tendons.17
Langberg et al18 measured blood flow in the peritendinous space of the human Achilles tendon at rest and after 40 minutes of dynamic contraction of the triceps surae. Blood flow in the peritendinous space 5 cm proximal to its insertion increased fourfold with exercise, while it increased only 2.5-fold when measured 2 cm proximal to the insertion.18 The increase in blood flow during exercise probably results from the considerable rise in the negative tissue pressure in the peritendinous space.19
Tenocytes and tenoblasts comprise 90–95% of the cellular elements of the tendon. Chondrocytes, vascular cells, synovial cells, and smooth muscle cells form the remaining cellular elements. The extracellular tendon matrix is composed of collagen and elastin fibres, ground substance such as proteoglycans, and organic components such as calcium.20,21
Collagen fibrils are bundled into fascicles containing blood, lymphatic vessels, and nerves,22 and have been shown to interchange between fascicles.23 The fascicles, which are surrounded by the endotenon, group together to form the gross structure of the tendon. The tendon is enveloped by a well defined layer of connective tissue, the epitenon. This, in its turn, is surrounded by the paratenon, with a thin layer of fluid in between to reduce friction during tendon motion. The innermost layer of the epitenon is in direct contact with the endotenon.
Actin and myosin are present in tenocytes,24 and tendons have almost ideal mechanical properties for the transmission of force from muscle to bone. Tendons are stiff and resilient, with high tensile strength: they can stretch up to 4% before damage.20,25 Achilles tendons in men have higher maximum rupture force and stiffness with a larger cross sectional area than in women, while younger tendons have significantly higher tensile rupture stress and lower stiffness.26
The peak Achilles tendon force and the mechanical work by the calf muscles is 2233 N and 34 J in the squat jump, 1895 N and 27 J in the counter movement jump, and 3786 N and 51 J when hopping.27 The indirect estimation of peak load on the Achilles tendon, normalised to subject body weight, is 6.1–8.2 × body weight during running, with a tensile force of more than 3 kN.28
The loads imposed on the Achilles tendon were measured using a “buckle”-type transducer implanted in the Achilles tendon under local anaesthesia. They reached up to 9 kN during running, corresponding to 12.5 times the body weight, 2.6 kN during slow walking, and less than 1 kN during cycling.12,29–32
A tendon loses its wavy configuration when it is stretched more than 2%. As collagen fibres deform, they respond linearly to increasing tendon loads.20,33 The normal wavy appearance of the tendon is regained if the strain placed on it remains at less than 4%. At strain levels greater than 8%, macroscopic rupture will occur.25,34,35
The causes of Achilles tendinopathy remain unclear.2,4 Various theories link tendinopathies to overuse stresses, poor vascularity, lack of flexibility, genetic make up, sex, and endocrine or metabolic factors (table 2).36
Excessive loading of the tendon during vigorous training activities is regarded as the main pathological stimulus.2,4,37 The Achilles tendon may respond to repetitive overload beyond physiological threshold by either inflammation of its sheath or degeneration of its body, or by a combination of the two.38 Damage to the tendon can occur even if it is stressed within its physiological limits when the frequent cumulative microtrauma applied do not leave enough time for repair.1 Microtrauma can result from non-uniform stress within the Achilles tendon from different individual force contributions of the gastrocnemius and soleus, producing abnormal load concentrations within the tendon and frictional forces between the fibrils, with localised fibre damage.39
Tendinopathy has been attributed to a variety of intrinsic and extrinsic factors. Tendon vascularity, gastrocnemius-soleus dysfunction, age, sex, body weight and height, pes cavus deformity, and lateral ankle instability are common intrinsic factors. Excessive motion of the hindfoot in the frontal plane, especially a lateral heel strike with excessive compensatory pronation, is thought to cause a “whipping action” on the Achilles tendon, and predispose it to tendinopathy.40 Also, an appreciable forefoot varus has often been found in patients with Achilles tendon problems.41 Perhaps for these reasons foot orthoses are advocated to control symptoms in chronic Achilles tendinopathy,42 although the scientific evidence from randomised controlled trials for their use is still lacking. Changes in training pattern, poor technique, previous injuries, footwear, and environmental factors such as training on hard, slippery, or slanting surfaces are extrinsic factors that may predispose the athlete to Achilles tendinopathy (table 3).4,37,43–45 It should be emphasised, however, that these are aetiopathogenetic theories, and a cause-effect relation has not been shown in longitudinal studies based on hypothesis testing.
The general pattern of intratendinous degeneration is common to the ruptured and tendinopathic tendons, but there is a greater degree of degeneration in the ruptured tendons. It is therefore possible that there is a common, as yet unidentified, pathological mechanism that has acted on the tendons, causing tendinosis46 and the clinical picture of tendinopathy.
Recently, changes in expression of genes important in cell-cell and cell-matrix interactions in Achilles tendinopathy have been reported, with downregulation of metalloprotease 3 mRNA in tendinopathic Achilles tendon samples. Levels of type I and type III collagen mRNAs were significantly higher in the tendinopathic samples than “normal”. Therefore, metalloprotease 3 may play an important part in the regulation of tendon extracellular matrix degradation and tissue remodelling.47
There appears to be little biochemical evidence of inflammation in degenerative tendon tissue. With the use of in vivo microdialysis, intratendinous measurements showed that glutamate levels were elevated in painful, degenerative tendon. There was no increase in inflammatory prostaglandin E2 .48,49
The pathological label “tendinosis” has been in use for more than two decades to describe collagen degeneration in tendinopathy.50 Despite that, many clinicians still use the term “tendinitis”, implying that the fundamental problem is inflammatory. We advocate the use of the term tendinopathy as a generic descriptor of the clinical conditions in and around tendons arising from overuse.51 The terms tendinosis and tendinitis should be used after histopathological examination.51
Tendinosis is defined by Jozsa and Kannus20 as intratendinous degeneration—that is, hypoxic, mucoid or myxoid, hyaline, fatty, fibrinoid, calcific, or some combination of these—from a variety of causes (ageing, microtrauma, vascular compromise, etc). Histologically, there is non-inflammatory intratendinous collagen degeneration with fibre disorientation and thinning, hypercellularity, scattered vascular ingrowth, and increased interfibrillar glycosaminoglycans20,36,52–54 (fig 1). Leadbetter53 proposed that tendinosis is a failure of cell matrix adaptation to trauma because of an imbalance between matrix degeneration and synthesis.1,53 Macroscopically, the affected portions of the tendon lose their normal glistening white appearance and become grey and amorphous. The thickening can be diffuse, fusiform, or nodular55 (fig 2).
The paratenon can be involved in the early phases of tendinopathy, and may present as “peritendinitis crepitans” due to adhesion between the tendon and the paratenon. Histologically, tendinosis shows partial disruption in tendon fibres. Tendinosis can be asymptomatic—for example, most patients with an Achilles tendon rupture did not have a clinical picture of tendinopathy before the rupture, and only histology reveals the profound intratendinous changes. Tendinosis may also coexist with symptomatic paratendinopathy.1,44
Four types of nerve endings can normally be identified in tendons: Ruffini corpuscles; free nerve endings; Pacini corpuscles mainly at the tendon site; the Golgi tendon organs mainly at the muscular site.56 The source of pain in tendinopathy is still under investigation. Classically, pain has been attributed to inflammatory processes, but, as it has become evident that tendinopathies are degenerative not inflammatory conditions, recently the combination of mechanical and biochemical causes has become more attractive.57,58 Tendon degeneration with mechanical breakdown of collagen could theoretically explain the pain mechanism, but clinical and surgical observations challenge this view.58 The biochemical model has become appealing, as many chemical irritants and neurotransmitters may generate pain in tendinopathy. High concentrations of the neurotransmitter glutamate have been found in patients with Achilles tendinopathy.59 The tendons in these patients showed no signs of inflammation, as indicated by the normal prostaglandin E2 levels.59 Substance P and chondroitin sulphate may also be involved in producing pain in tendinopathy.57,58
The commonest form of tendon healing is by scarring, which is inferior to healing by regeneration.20,53 A tendon heals in essentially the same way as soft tissue, going through the same inflammatory (1–7 days of injury), proliferative (7–21 days), and remodelling (three weeks to one year) phases. Despite collagen maturation and remodelling, tendons are biochemically and metabolically less active than bone and muscle.20,53 Fibroblasts synthesise collagen type III in the proliferative phase. This will be replaced gradually by collagen type I from day 12–14 with progressive increase in tensile strength.20
In animals, by 15 days after surgery, the healing tendons regain 48% tensile strength, 30% of energy absorption, 20% tensile stress, and 14% Young's modulus of elasticity of intact tendons. Healing tendons have 80% of the collagen and 60% of the collagen cross links (hydroxypyridinium) of normal tendons. Healing tendons yield more soluble collagen than intact tendons. This has led to the hypothesis that increased collagen synthesis takes place, possibly with enhanced resorption of mature collagen in healing tendons compared with intact tendons. Electron microscopy shows ultrastructural differences between intact and healing tendons.60
Recovery from tendon injury is slow because of many factors, including low oxygen consumption, slow synthesis of structural protein, and excessive load. The oxygen consumption of tendons is 7.5 times lower than that of skeletal muscles, and tendons are able to sustain loads of up to 17 times body weight.61 recent studies have shown that the healing capacity of tendons may have been underestimated.62
Overuse tendon conditions have traditionally been considered to result from an inflammatory process and treated as such. However, microscopic examination of abnormal tendon tissues shows a non-inflammatory degenerative process.55,63 The present evidence suggests that, in overuse tendinopathy, so-called “tendinitis” is rare. It may occur occasionally in the Achilles tendon in association with a primary tendinosis.
Histopathology of Achilles tendinopathy shows degeneration, a disordered arrangement of collagen fibers, and an increase in vascularity,64–66 with a singular absence of inflammatory cells and a tendency to poor healing.67 An angioblastic reaction is present, with random orientation of blood vessels, sometimes at right angles to collagen fibres.68 Frank inflammatory lesions and granulation tissue are uncommon and, when found, are associated with tendon ruptures.67
At least six different subcategories of collagen degeneration have been described,69 but Achilles tendon degeneration is usually either “mucoid” or “lipoid”.20 Alcian blue staining shows increased ground substance.20 The characteristic hierarchical structure of collagen fibres is also lost.70,71
Many factors are associated with the pathogenesis of a tendinopathy. These include tissue hypoxia with consequent free radical induced tendon changes caused by ischaemia-reperfusion injury,72 and exercise induced hyperthermia.73 Further, a tendon that has been strained repeatedly to more than 4% of its original length loses elasticity and is at increased risk of a subsequent break in the collagen structure.74 Aged tendons, on the other hand, show little evidence of degeneration. Normal ageing of connective tissue is morphologically different from degeneration.75 Aged tissue has a low rate of metabolism, progressively decreasing elasticity, and low tensile strength.
In 163 patients (75% of whom participated in non-professional sports, particularly running) with classical symptoms and signs of Achilles tendinopathy for a median of 18 months (range three months to 30 years), obvious changes in collagen fibre structure with loss of the normal parallel bundles were evident.76 In those subjects with macroscopic partial ruptures at surgery, fibrin deposits bordered frayed tissue, but histopathology remained identical with those cases without rupture.
This type of Achilles tendon degeneration is evident as increased signal on magnetic resonance imaging (MRI)77–82 and hypoechoic regions on ultrasound (US).57,80,83,84 These areas of abnormal imaging correspond to areas of altered collagen fibre structure and increased interfibrillar ground substance, which have been shown to consist of hydrophilic glycosaminoglycans.69,84,85
In the paratenon, mucoid degeneration, fibrosis, and vascular proliferation with a slight inflammatory infiltrate only have been reported.10,25,57,86–98 Astrom and Rausing76 found virtually no evidence of paratenonitis in their series of Achilles tendon specimens. These differences may be explained by the fact that Kvist et al92–94 did not report pathology of the tendon itself, and studied more active, younger patients. Thus, paratenonitis is not a prerequisite for Achilles tendon symptoms in a population of recreational sportspeople and office workers. The major lesion in chronic Achilles tendinopathy “is a degenerative process characterized by a curious absence of inflammatory cells and a poor healing response”.76 However, the degenerative process is active, with changes in cell activity and phenotype with an increased turnover of matrix and a different expression of genes compared with normal tendons.47
History and examination play a key role in diagnosis and management of Achilles tendinopathy. The onset of pain, duration, and aggravating factors should be documented. Thorough enquiry should be made into the relation of pain to various activities, intensity of training, and exercise technique. Details of previous treatments are also important.
Achilles tendinopathy typically presents with pain 2–6 cm proximal to the tendon insertion after exercise. As the pathological process progresses, pain may occur during exercise, and, in severe cases, the pain interferes with activities of daily living.99 There is good correlation between the severity of the disease and the degree of morning stiffness. Runners experience pain at the beginning and end of a training session, with a period of diminished discomfort in between.100
Clinical examination should start by exposing both legs from above the knees, and the patient should be examined standing and prone. The foot and the heel should be inspected for any malalignment, deformity, obvious asymmetry in tendon size, localised thickening, Haglund heel, and any previous scars. The Achilles tendon should be palpated to detect tenderness, heat, thickening, nodularity, and crepitation.101 The tendon's excursion is assessed. The “painful arc” sign helps to distinguish between tendon and paratenon lesions. In paratendinopathy, the area of maximum thickening and tenderness remains fixed in relation to the malleoli from full dorsiflexion to plantar flexion, whereas lesions within the tendon move with ankle motion.88 Patients with more chronic tendinopathy may have greater difficulty in performing the test than patients who present more acutely,101 although we have not found this test helpful in clinical practice.
US and MRI are the current imaging modalities of choice in patients with Achilles tendinopathy.77,79,102 Historically, in the reports by von Saar103 and Karger,104 radiography provided useful information on the involved Achilles tendon. Although plain soft tissue radiography is no longer the imaging modality of choice in tendon disorders, it still has a role in diagnosing associated or incidental bony abnormalities.
Archambault et al105 used a simple US grading scheme for patients with Achilles tendinopathy: grade 1, normal tendon; grade 2, enlarged tendon; grade 3, a tendon containing a hypoechoic area, regardless of size. The visualised imaged hypoechogenic regions can be nodular, diffuse, or multifocal. The ability to visualise the paratenon and intratendinous areas is dependent on the frequency probes used. Higher frequencies (7.5, 10, 13, and 15 MHz) are more accurate in visualising abnormality lesions in the Achilles tendon main body and paratenon.106
The hypoechoic regions (fig 3) correlate well with macroscopic pathology seen at surgery.77,79,107 However, ultrasonography cannot differentiate partial tendon ruptures from focal degenerative areas.80 Hyperechogenic areas can represent focal accumulation of calcium deposits (fig 4). Movin et al84 used the US guided core biopsy technique to compare the histopathology of biopsy specimens from hypoechoic areas with those from normoechoic areas within Achilles tendon with a clinical diagnosis of tendinopathy. The hypoechoic areas showed a very abnormal tendon structure including an increased amount of proteoglycans. However, moderate pathology was also found in the neighbouring normoechogenous areas within the same tendon, indicating a more generalised disorder than that depicted by US with a 7 MHz transducer.84
Patients with tendinopathy of the main body of the Achilles tendon with normal US findings had a shorter time to full recovery than those with enlarged tendons and intratendinous hypoechogenicity.105 Tendon enlargement and alterations in echotexture are risk factors for rupture in patients with Achilles tendinopathy.108
US is routinely used in Europe and is still regarded by many as a primary imaging method for the study of the Achilles tendon, as it correlates well with histopathological findings despite being operator dependent.5,84 The combination of imaging and clinical diagnosis enhances the efficiency of preoperative planning.102,109 US has interactive facilities, which help to reproduce symptoms by transducer compression and concentrate on the pathological area.110
The simultaneous use of colour Doppler with US can allow visualisation of regions of increased vascularity.111 Core biopsies guided by US allow analysis of the pathology of the tendinopathy.112,113 US guided invasive procedures such as percutaneous longitudinal tenotomy can direct management.114
Although US can show alterations in the Achilles tendon with high specificity and sensitivity, it has, like MRI, a high incidence of false positive findings,115 with mild to moderate changes observed in both the involved and uninvolved Achilles tendons not clearly related to the patients' symptoms.116 After surgery, US does not appear to be able to differentiate patients who make a good recovery from those with tendon symptoms.
The normal Achilles tendon is usually dark on all imaging sequences.117 In patients with pain located to the main body of the Achilles tendon, MRI may show (a) a thickened paratenon, (b) peritendinous fluid, (c) oedema of Kager's fat pad, (d) thickening of the tendon commonly in a fusiform shape, (e) focal or diffuse intratendinous intermediate or high signal, and (f) interrupted appearances of tendon tissue.78,118
Lesions in the Achilles tendon and in the peritendinous tissues can present in a similar fashion. MRI can help to differentiate between these.78 However, there is significant overlap of MRI findings in symptomatic and asymptomatic Achilles tendons. Furthermore, there is a spectrum of disease in symptomatic tendons, ranging from subtle intratendinous and peritendinous signal to complete tendon tear.119
The normal anatomy of the asymptomatic Achilles tendon is variable, and may be a potential source of diagnostic error.82 An abnormal signal without change in tendon thickness must be interpreted with caution, as the magic angle phenomenon can result in a false positive high signal intensity of normal tendon tissue.120
The sensitivity to depict pathological Achilles tendon tissue can be increased by shortening the echo time121 and by enhancement with a gadolinium contrast agent.84 Under optimal imaging conditions, tendon infrastructure can be evaluated.
MRI can depict the pathology in great detail.78 However, therapeutic guidelines based on MRI are lacking, and its importance in clinical decision making has not been established. The main disadvantage of MRI is its cost, and therefore US has become the primary imaging method in clinical practice in Europe and the Southern hemisphere. Given the high sensitivity of these imaging modalities, an abnormality should be interpreted with caution and correlated with the symptoms before any management recommendations are made.36
Surgical management of chronic Achilles tendinopathy and its healing resulted in a decrease or elimination of the intratendinous signal alteration, including static gadolinium enhanced T1 weighted images, correlating with an improved clinical outcome two years after surgery.81
Shalabi et al81 studied the early dynamic enhancement of the tendon signal with gadolinium contrast agent in patients with Achilles tendinopathy. Early enhancement of the intratendinous signal correlated at histopathology with very abnormal tendon tissue and foci of tenocytes with rounded nuclei. Two years after surgery, the early contrast enhancement of the tendon signal had diminished or disappeared.81
Tendinopathy can probably be prevented by encouraging athletes and coaches to follow a sensible training programme.122 Seeking medical attention at an early stage may Boost outcome, as treatment becomes more complicated and less predictable when the condition becomes chronic.86,123,124
At present, management of tendinopathy is more an art than a science.36 The efficacy of a conservative rehabilitation programme is debatable. Patients with Achilles tendinopathy (n = 70), with a duration of symptoms of less than six months, were randomised to treatment with either a non-steroid anti-inflammatory drug (piroxicam) or placebo. Both groups received adjunct treatment with a period of rest combined with stretching and strengthening exercises. The overall result after one month was identical, with a rate of success slightly better than 50%.125 Favourable long term prognosis has been reported with a comprehensive conservative protocol that included relative rest, anti-inflammatory drugs, physiotherapy, and orthoses.75,96,116,126,127 Nevertheless, some authors argue that conservative management of chronic Achilles tendinopathy can be time consuming and often unsatisfactory.13
Abstention from the activities that caused the symptoms is recommended in the acute phases. In mild tendinopathy, relative rest or modified activities are prescribed.128 Collagen fibres repair, and remodelling is stimulated by tendon loading. Therefore complete rest of an injured tendon can be counterproductive.
Cyriax97 regarded deep friction massage as a most important technique. In chronic tendinopathy, this should be accompanied by stretching to restore tissue elasticity and reduce the strain in the muscle-tendon unit with joint motion. Augmented soft tissue mobilisation (ASTM) is a new non-invasive soft tissue mobilisation technique which has been successfully used in the treatment of chronic tendinopathy, probably through controlled application of microtrauma.129,130 In a collagenase induced tendinopathy model in rat, light microscopy showed increased fibroblast proliferation with this treatment.129,130
Gentle static stretching by pulling, holding, and releasing the gastrocnemius-soleus complex is the best way of stretching. Another effective way of stretching is by using a wall, stair, or 20° inclined board.44
Eccentric strengthening of the gastrocnemius-soleus muscle and loading of the Achilles tendon are important for both prevention and conservative management of Achilles tendinopathy.131,132 As a rule, gentle strength training should be started early after injury to prevent disuse atrophy, and should not be painful.44
In a prospective multicentre study of 44 patients, with 22 patients (12 men, 10 women; mean age 48 years) in each treatment group, the patients were instructed to perform either eccentric or concentric training on a daily basis for 12 weeks. In both types of treatment regimen, the patients were encouraged to undertake their exercises despite experiencing pain or discomfort in the tendon during exercise. After the eccentric training regimen, 18 of 22 (82%) patients were satisfied and had resumed their previous activity level, compared with eight of 22 (36%) patients who had performed concentric training (p<0.002).
Correction with orthotics can alter the biomechanics of the foot and ankle and relieve heel pain.134,135 Therefore orthotics are commonly used, especially in runners, with up to 75% success.136–140 A heel lift of 12–15 mm is classically used as an adjunct to the management of Achilles tendon pain .75 However, in a randomised prospective trial of three forms of conservative treatment of sports induced Achilles tendinopathy, the claimed benefit of viscoelastic pads was not substantiated.141
Cryotherapy is used to reduce the metabolic rate of the tendon and to decrease the extravasation of blood and protein from new capillaries found in tendon injuries.142 It also has an analgesic effect.
Therapeutic ultrasound may reduce the swelling in the acute inflammatory phase and experimentally Boost tendon healing.98,143,144 Ultrasound also stimulates collagen synthesis in tendon fibroblasts and stimulates cell division during periods of rapid cell proliferation.145
Several drugs, such as low dose heparin, wydase, and aprotinin, have been used in the management of peritendinous and intratendinous pathology.146–149 Although widely used and promising, evidence of their long term effectiveness is still unclear. Peritendinous injections with corticosteroids are still controversial. One randomised controlled study of 28 patients showed that methyl prednisolone acetate did not Boost the cure rate or shorten the healing time in patients with Achilles tendinopathy.150 There is insufficient evidence comparing the risks and benefits of corticosteroid injections in Achilles tendinopathy,151 and some authors believe that steroid injections do not increase Achilles tendon rupture rate.152 Others, however, have shown that intratendinous injections of corticosteroid in animals reduced tendon strength with a potential risk of rupture for several weeks after injection.151–155
The natural history of Achilles tendinopathy is still unclear: 24–45.5% of subjects with Achilles tendon problems that fail to respond to conservative management will undergo operative management.13,156 In an eight year longitudinal study of conservative management of patients with Achilles tendinopathy, 24 of the 83 patients (29%) had to be operated on. Seventy patients (84%) had full recovery of their activity level. At eight years, 78 patients (94%) were asymptomatic or had only mild pain with strenuous exercise. However, 34 patients (41%) started to suffer from Achilles tendinopathy in the initially uninvolved contralateral tendon.116,127
Surgery is recommended after exhausting conservative methods of management, often tried for at least six months. However, long standing Achilles tendinopathy is associated with poor results, with a greater rate of reoperation before reaching an acceptable outcome.157 In general, surgical procedures can be broadly grouped into four categories: open tenotomy, with removal of abnormal tissue and the paratenon not stripped; open tenotomy, with removal of abnormal tissue and the paratenon stripped; open tenotomy, with longitudinal tenotomy with or without paratenon stripping; percutaneous longitudinal tenotomy.5,87,158–160 The objective of surgery is to excise fibrotic adhesions, remove degenerated nodules, and make multiple longitudinal incisions in the tendon to detect intratendinous lesions, restore vascularity, and possibly stimulate the remaining viable cells to initiate cell matrix response and healing.4,5,38,86,96,158 The reasons why multiple longitudinal tenotomies work are still unclear. recent investigations show that the procedure triggers neoangiogenesis at the Achilles tendon, with increased blood flow.161 This would result in improved nutrition and a more favourable environment for healing.
At surgery, the crural fascia is released on both sides of the tendon. Adhesions around the tendon are then trimmed, and the hypertrophied paratenon is excised.13 In addition, longitudinal splits are made in the tendon to identify the abnormal tendon tissues and excise the areas of degeneration. Reconstruction procedures may be required if large lesions are excised.162
We perform the operation on an outpatient day case basis. The patient is examined before the operation to correctly identify and mark the area of maximum tenderness and swelling. We normally do not use a tourniquet but lift the end of the operating table 15–20°.163 The patient lies with ankles resting on a sandbag or a pillow and feet hanging over the end of the operating table. A longitudinal slightly curved incision is centred over the abnormal part of the tendon and placed medially, with the concave part toward the tendon. If a lateral approach is used, care should be exerted to avoid the sural nerve and the short saphenous vein.43,164
The paratenon and crural fascia are incised and dissected from the underlying tendon. If necessary, the tendon is freed from adhesions on the posterior, medial, and lateral aspects. The paratenon should be excised obliquely, as a transverse excision may produce a constriction ring that may require further surgery.88 The fatty tissue anterior to the tendon should be left intact, as the mesotenon contained within it is an important source of vascular supply to the tendon. Areas of thickened, fibrotic, and inflamed paratenon are excised (fig 5). Inspection of areas lacking normal lustre and careful palpation for thickening, softening, or defects reveals local sections corresponding to areas of tendinosis within the tendon. These zones can be explored with longitudinal tenotomies. The pathology is identified by the change in texture and colour of the tendon. The lesions are then excised, and the defect can either be sutured in a side to side fashion or left open; we normally leave it open. If extensive debridement is required, it is possible to use a turndown flap of the aponeurosis of the medial or lateral head of the gastrocnemius to repair the defect. Occasionally, a tendon transfer may be required if the excision of the degenerated area has left a major defect in the tendon (>50%).165 In most cases, the lesions will be well localised, with normal tendon in between.
In patients with associated insertional lesions or retrocalcaneal bursitis, an extended approach is used. A full inspection may show an enlarged, inflamed, or scarred retrocalcaneal bursa, adherent to the anterior surface of the Achilles tendon. There may be, in addition, fluid or loose fibrinous bodies within the bursa. After excision of this area, inspection of the posterior superior angle of the calcaneum allows visualisation of any impingement with the insertion of the Achilles tendon with dorsiflexion. This area can be removed with an osteotome, and the sharp edges removed with a rasp. If used, the tourniquet can be deflated, and haemostasis achieved by diathermy. A below knee lightweight cast is applied with the foot plantigrade, and immobilisation is continued for two weeks. Patients are encouraged to weight bear as soon as possible. Greater protection is recommended in patients needing tendon reconstruction. At two weeks, the cast is removed and stretching exercises are started. Sport specific training is started at three months, and competition is resumed at six months.
In patients with isolated Achilles tendinopathy with no paratendinous involvement and a well defined nodular lesion less that 2.5 cm long, we have used multiple percutaneous longitudinal tenotomies when conservative management has failed. A US scan can be used to confirm the precise location of the area of tendinopathy. Patients are mobilised as soon as possible.164,166 If the multiple percutaneous tenotomies are performed in the absence of chronic peritendinopathy, the outcome is comparable to that of open procedures. In addition, it is simple and can be performed in the clinic under local anaesthesia without a tourniquet, but attention to detail is necessary, as even in minimally invasive procedures complications are possible (fig 6).
More recently, we performed US guided percutaneous longitudinal tenotomy under local anaesthesia after failure of conservative management. The procedure had a relatively high rate of success in patients with a single, well defined area of tendinopathy of the main body of the tendon and no paratenon involvement. In patients with diffuse or multinodular tendinopathy or with pantendinopathy, a formal surgical exploration with stripping of the paratenon and multiple longitudinal tenotomies may be preferable.114
Longitudinal tenotomies increase the blood supply of the degenerated area.161 Recently, in a rabbit model, after longitudinal tenotomy we performed a soleus pedicle graft within the operated tendon, and showed that the transplanted muscle was viable and had integrated well within the tendon tissue three months after the transplant, without transforming into connective tissue. Hypervascularisation of the graft tissue, probably resulting from the operation, was also observed, together with neoangiogenesis up to three months after the operation.64
It is remarkable how, for a condition that is relatively common, most studies did not report their assessment procedure, which makes it difficult to compare the results.167 Most authors report excellent or good result in up to 85% of cases, and most articles reporting surgical success rates have over 70% of successful results.46,167 Schepsis and Leach160 report good results in patients with paratendinitis and mucoid degeneration. Kvist44 reports good and excellent results of both paratendinitis and tendinosis. However, this is not always observed in clinical practice.124 In a recent systematic review of the published results of surgery for Achilles tendinopathy, we found an inverse relation between reported success rates and the quality of the scientific methodology used in the study.167 The most common complication of operative management of Achilles tendinopathy is skin breakdown, but deep vein thrombosis and lesions to the sural nerve have been reported.
In the most comprehensive study to date, 432 consecutive patients were followed up longitudinally for five months after surgery. If a complication arose, the patient was followed up clinically for at least one year. There were 46 (11%) complications in the 432 patients, and 14 patients with a complication had a reoperation. However, most patients with a complication healed and returned to their previous levels of activity127 (fig 7).
The long term effects of operative management are still not fully clarified. The relative underuse of the affected lower limb after surgery results in prolonged calcaneal bone loss despite early weightbearing loading in patients surgically treated for chronic tendinopathy of the main body of the Achilles tendon. The bone loss had not recovered one year after surgery, but in a comparison group there were no significant side to side differences 39.5 months after the operation.168 Also, the deficit in calf muscle strength seen on the injured side before surgery in this group of patients remained despite them being pain free and active in sports or at recreational level five years after the operation. However, the percentage side to side difference is relatively small, and may not have any clinical relevance.169
Several quantitative tests of ankle function170 have been used to measure outcome in Achilles tendinopathy. However, condition specific numerical scales generally have greater sensitivity and specificity than do general purpose scales.171 A specific scale for patients with patellar tendinopathy172 has been published, and we have devised a self administered questionnaire based instrument to measure the severity of Achilles tendinopathy, the VISA-A.171 There is a need for a quantitative index of pain and function in patients with Achilles tendinopathy. The VISA-A questionnaire appears to be a valid, reliable, and easy to administer measure of the severity of Achilles tendinopathy, and seems suitable for both clinical rating and quantitative research.
Although Achilles tendinopathy is common, experimental models for its study and treatment are uncommon.172–176 For example, Backman et al172 produced a paratendinopathy with involvement of the main body of the tendon, which showed degenerative changes and increased number of capillaries, by prolonged repeated contractions of the triceps surae (up to six hours per session, three times a week, for up to six weeks) resulting from electrical stimulation producing movements of the ankle joint in anaesthetised rabbits.
In three month old male rats subjected three times a week for one hour to eccentric exercise of one triceps surae muscle (30 stimulations/min) under general anaesthesia, inflammation of the epitenon and paratenon could be induced, but tendon changes corresponding to chronic tendinosis did not develop despite 11 weeks of this regimen.173
More recently, rats have been used to produce acute Achilles tendinopathy by direct trauma.174 In rats, a single intratendinous injection of cytokines produced mild, reversible tendon injury, with no matrix damage or evidence of collagen degradation.176 Again in rats, more prolonged administration of proinflammatory cytokines resulted in diffuse extracellular matrix involvement and collagen fibre derangement and degradation.178
Many clinical and biological aspects of Achilles tendinopathy are still unclear. It is classically considered an overuse injury. Nevertheless, some patients seem to be more prone to it than others despite similar training and competition loads. With advances in molecular biology, it may be possible to identify the factors that influence tenocyte metabolism and promote the natural healing process. The role of growth factors in tendon healing is still unclear, although there is evidence that basic fibroblast growth factor can stimulate tendon healing by promoting cell proliferation and matrix synthesis.177 Application of the appropriate growth factors at certain periods during the repair process may Boost healing of tendon lesions. However, most of these growth factors are proteins which are rapidly metabolised,178,179 and their delivery is challenging and difficult. Transfer of growth factor genes into tenocytes may eliminate this problem by continuous local release of growth factors at the healing site. Gene transfer for the targeted delivery of growth factors has been used successfully in animal studies,180,181 and transfer of growth factor genes into tenocytes may eliminate this problem by continuous local release of growth factors at the tendinopathy site.182
Achilles tendinopathy is prevalent and potentially incapacitating in athletes involved in running sports. It is a degenerative, not an inflammatory, condition, and, until its biology has been elucidated, its management will be based more on empirical than scientific principles.
Although Achilles tendinopathy has been extensively studied, there is a clear lack of properly conducted scientific research to clarify its causes, pathology, and optimal management plan.
The outcome of Achilles tendinopathy is more favourable when treated within six months of onset. Most patients respond to conservative measures if the condition is recognised early, whereas continuing the offending activities leads to adhesion and chronic changes which are more resistant to conservative treatment. Teaching patients to control the symptoms may be more beneficial than leading them to believe that Achilles tendinopathy is fully curable. Progressive eccentric training has been reported with encouraging short term results.
Surgery usually involves removal of adhesions and degenerated areas and decompression of the tendon by tenotomy or measures that influence the local circulation.
It is still debatable why tendinopathic tendons respond to surgery.58 For example, we do not know whether surgery induces revascularisation, denervation, or both, resulting in pain reduction. It is also unclear how longitudinal tenotomy improves vascularisation.
As the biology of tendinopathy is being clarified, more effective management regimens may come to light, improving the success rate of both conservative and operative management.
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The sports of boxing and karate expose athletes to severe head impacts and the risk of brain injury.1 In many cases, the athlete is exposed to repeated impacts and injuries. In a 16 year study of injuries to professional boxers in Australia, 107 injuries were reported in 427 fight participations from August 1986 through to August 2001.2 The most commonly injured body region was the head and neck (89.9%). In this body region, injuries to the eye were the most frequent (45.8%) followed by concussions (15.9%). There was no information on the mechanism or forces that caused the injuries.
The principles of momentum and energy conservation have been used to estimate the force of various punches and to understand what causes head injuries in karate and boxing. Peak punch forces are reported to range from 1666 to 6860 N.3 Walker4 estimated that a force of 3200 N is required to break a brick, which is common practice in karate demonstrations. However, in many studies the momentum of the punch was not transferred to an object comparable in mass and biofidelity to the human head and neck, and thus the risk of injury cannot be estimated from these punches.
In a study of karate, Smith and Hamill5 measured the fist velocities from punchers of different skill levels and the relative momentum of a 33 kg punching bag. Punches to the bag with bare fists (BF), karate gloves (KG), and boxing gloves (BG) were recorded with high speed film. The mean bag momentum for all tests was 47.37 Ns. The results showed no significant differences in fist velocities between skill levels or glove type (BF: 11.03 (standard deviation (SD) 1.96) m/s, KG: 11.89 (SD 2.10) m/s, BG: 11.57 (SD 3.43) m/s). The average fist velocity was 11.5 m/s. Differences in bag momentum were found with changes in skill level and glove. Greater bag momentum was generated with boxing gloves (53.73 (SD 15.35) Ns) than with either bare fists (46.4 (SD 17.40) Ns) or karate gloves (42.0 (SD 18.7) Ns), which had approximately the same momentum. The bag momentum was also greatest for the highest skilled subjects (60.8 (SD 17.3) Ns) compared to the lower skilled punchers (42.3 (SD 11.6) Ns) even though their respective fist velocities were approximately the same. The authors hypothesised that the increase in bag momentum was due to the skilled boxer’s ability to generate a greater effective mass during the impact than the lower skilled boxers. With a fist velocity at 11.5 m/s immediately before impact and the resultant bag momentum of 47.4 Ns, the effective mass of the striking fist was estimated to be approximately 4.1 kg. This is greater than the mass of the hand and reflects the ability to link more of the arm mass into the punch.
Atha et al6 collected punch force data on a world ranked heavyweight boxer using an instrumented target suspended as a ballistic pendulum. The target was a cylindrical metal mass of 7 kg, estimated to be the mass of the head and neck of a heavyweight boxer. During the punch, the boxer’s fist reached an 8.9 m/s impact velocity with a resulting peak impact force of 4096 N. The peak acceleration of the pendulum was 53 g. Only one boxer participated in the study so extrapolations to the general boxing population are not possible. However, the results reflect the force of a heavyweight boxer’s punch. The model’s biofidelity is unknown, so the risk of injury can not be determined. Other studies on punch force have reported peak loads of 4800 (SD 601) N for elite, 3722 (SD 375) N for intermediate, and 2381 (SD 328) N for novice English boxers7 and 3453 N for 24 elite, 3023 N for 23 national, and 2932 N for 23 intermediate boxers.8
Smith et al9 evaluated the punch of three amateur boxers to assess head impact responses and the risk of injury. Each boxer was instructed to strike a headform with a left hook or left jab. The headform was instrumented with a 3-2-2-2 configuration of accelerometers10 to determine the translational and rotational acceleration. The translational acceleration averaged 21.5 (SD 4.6) g for the left jab and 43.6 (SD 15.6) g for the left hook. The rotational acceleration varied from 292.7 (SD 72.2) rad/s2 for the left jab to 675.9 (SD 230.6) rad/s2 for the left hook. Based on the tolerance limit of 200 g for translational acceleration and 4500 rad/s2 for rotational acceleration, the researchers concluded that neither the translational nor the rotational acceleration reached a level that was injurious to the boxer. They suggested that repeated sub-concussive blows were the injury mechanism for mild traumatic brain injury (MTBI).
In contrast, Johnson et al11 reported that head injuries are likely in boxing. Low velocity volunteer data were extrapolated to predict higher impact velocities that were more representative of those seen in boxing. The report determined that the extrapolated data fell in the region of “certain injury” calculated by Unterharnscheidt and Sellier.12 Their study assumed that the maximum punch strength was delivered to the head with only minimal deflection. While the results of this study raise concerns about the risk of injury in boxing, the use of extrapolated low speed data to predict injury at higher speeds needs additional validation.
Techniques have been developed to determine the risk of head injury for an impact.13 Based on biomechanical principles, these techniques involve the simulation of real-world impacts on human surrogates with biofidelity built into their impact responses.14 Biofidelity reflects the ability of the surrogate to simulate the essential biomechanical characteristics of the human impact response. The Hybrid III crash dummy used in this project is currently the most advanced, validated biomechanical surrogate for frontal impacts. The validation of the surrogate includes the head and neck, which are of particular interest in this study. The head and neck of the Hybrid III are used in many state of the art side impact dummies, so its use includes a range of impact angles. Sensors placed in the surrogate collect biomechanical data that can be related to risk of injury. Previous studies have developed a collection of criteria to estimate the risk of injury from impacts.15–17
The techniques used to estimate the risk of head injury are based on relating measured responses to observed injuries. The first to establish a relationship between head injury and translational acceleration was Lissner.18 His observations led researchers to develop a curve relating the level of acceleration and the duration of the impact to the risk of head injury.13,19 Based on these initial observations, it is currently believed that impacts to the head that have low peak acceleration require longer pulse durations to cause the same injuries as those with high peak acceleration. The head injury criterion (HIC) currently used to assess risks is calculated as follows:
where t1 and t2 are determined to give the maximum value to the HIC function and a(t) is the resultant translational acceleration of the head centre of gravity (cg). In practice, a maximum limit of T = t2−t1 = 15 ms is used.
The US delegation to the ISO working group 6 provided an estimate of the percentage of the adult population expected to experience a life threatening brain injury (Abbreviated Injury Scale 4) for various HIC levels due to frontal head impacts.20 The delegation’s best estimate was that 16% of the adult population would experience a life threatening brain injury at an HIC level of 1000. In recent studies of concussions in National Football League (NFL) players in the USA, Pellman et al21,22 recommended a value below 250 to minimise the risk of MTBI or concussion.
The British physicist Holbourn23 worked with gel models of the brain and was one of the first to cite rotational acceleration as an important mechanism in head injury. Ommaya and Hirsch24 scaled primate head injury data to humans and predicted that a level of head rotational acceleration in excess of 1800 rad/s2 would have a 50% probability of resulting in cerebral concussion in man. Analysis of injuries produced in rhesus monkey experiments resulted in Gennarelli et al25 estimating a 16 000 rad/s2 rotational acceleration tolerance threshold in man.
In a survey of their research on rotation head injury, Ommaya et al17 stated that for the adult brain the rotational acceleration required to produce concussion is 4500 rad/s2 and severe diffuse axonal injuries (DAI) occur at rotational accelerations of 18 000 rad/s2. This response range is obtained from scaling animal impact data and indicates the difficulty in developing a precise injury prediction criterion for rotational motion. One problem is that the shape and mass of the animal and human brains are different and scaling laws assume geometric similarity. The low mass of the animal brain requires very high rotational accelerations to produce closed head injuries.24 These factors complicate predictions of human injury from animal responses to rotational head acceleration.
In an effort to understand the relationship between forces delivered to the jaw region and the risk of head injury from translational and rotational acceleration, the biomechanics of boxer’s punches were studied. Olympic class boxers threw straight punches at an instrumented Hybrid III headform with their dominant hand. Punch force, weight class, and the severity of head translational and rotational acceleration were correlated.
Ten Olympic boxers participated in the study, but only data from seven were suitable for analysis and inclusion. Each boxer delivered three punches that were evaluated for strength and severity. The weight of the boxers ranged from 112 lbs (48 kg) to 240 lbs (109 kg) and represented four weight classes. After each boxer had warmed up, they were instructed to strike the lower third of the headform on the instrumented dummy with their gloved fist three times using maximum effort. The exact impact location of the punch was determined and translational force, hand velocity, and effective mass were calculated. The severity of the impacts was quantified using rotational acceleration, HIC, and head velocity.
Because a frangible face is used,26 the dummy’s impact response is similar to that of a human, so the risk of injury to the boxer’s hand was no greater than the risk during normal training. However, although the risk of injury was minimal, a certified boxing trainer was present throughout the testing and informed written consent was obtained from each boxer. Since their involvement in the study was voluntary, it was made clear that the boxers could withdraw from the study at any time. The boxers were not compensated for their participation. Approval for the study was obtained through Wayne State University’s Human Investigation Committee.
A Hybrid III dummy with a frangible faceform was used to represent the response of the jaw and realistically transfer acceleration to the head. Figure 1 (right) shows the side view of the frangible face and Hybrid III headform.26 It has an improved biomechanical response in the facial region over the standard moulded Hybrid III and is capable of more accurately reproducing the force and acceleration measurements of the head for impacts in the frontal, zygomatic, maxillary, and mandibular regions. Other devices have used either a stiff load measuring face or deformable structures in regions other than the jaw.27,28 The frangible Hybrid III dummy headform was attached to the Hybrid III neck and upper torso to ensure realistic headform motion (fig 1, left). Olympic headgear was placed on the dummy, but the blows were straight to the face and did not engage the headgear. For positioning, the upper torso was attached to a rigid table with a foam pad placed below the Hybrid III abdomen insert so that the dummy remained in an upright position during the impact.
The Hybrid III simulates a tensed neck so the head is normally upright. The segmented neck includes flexible polymer discs to simulate the flexion-extension and lateral bending responses. A cable inside tightens the assembly to give the correct neck response in calibration testing and during head acceleration.29,30 Although the Hybrid III neck was utilised in the study, it is not known if it accurately represents the strength of a boxer’s neck since a boxer undergoes extensive training to develop the neck muscles necessary to resist the force delivered by an opponent. Johnson et al11 demonstrated that neck muscle tension has little effect on the oscillation of the head under sinusoidal excitation from a shaker.
Accelerometers were placed in the boxer’s hand and in the head of a Hybrid III dummy to measure the impact dynamics. Seven Endevco (San Juan Capistrano, CA) 7264-2000 accelerometers were used in the evaluation; two were secured to the boxer’s hand to measure overall hand dynamics, while the remaining five were fastened to a bar within the headform for calculation of translational and rotational acceleration using the in-line technique.31,32 Figure 2 shows the two accelerometers attached to a magnesium block that was tightly taped to the clenched fist to measure hand dynamics. The two accelerometers were at right angles so that the resultant hand acceleration could be measured. A third accelerometer was not included because it was assumed that the lateral acceleration of the hand was negligible for a straight punch. The block was secured to the boxer’s hand by embedding it in the boxer’s hand wrap. This fixation method did not rigidly attach the accelerometers to the hand but did provide a good method of predicting the overall hand dynamics.
A six axis upper neck load cell (Denton ATD, Rochester Hills, MI) was used to measure neck loads and moments. An event switch was attached to the face of the dummy and determined initial glove/headform contact giving a precise initiation signal establishing time zero. The primary data acquisition system was the IDDAS data acquisition system (SoMat, Urbana, IL). Data were collected at a sampling rate of 14.7 kHz and post processed according to SAE J211/1.33
A Tekscan pressure sensor (Boston, MA) was inserted between the frangible faceform and the headform skin to measure the force on the jaw region. The Tekscan pressure system measures the pressure and contact area of a punch applied to any section of the sensor. The Tekscan sensor (Model 9500) used was an extremely thin (∼0.1 mm), flexible tactile pressure sensor capable of measuring pressures ranging from 0 to 2000 psi. The size of the sensing surface was 71×71 mm with a resolution of 3.9 sensors per centimetre. The sampling rate of the system was 1.4 kHz. The output from the system is the pressure distribution on the surface of the sensor and the corresponding contact area as a function of time. The force on any region is calculated by multiplying the sum of the individual pressure measurements within the selected region by the active contact area. The Tekscan and IDDAS systems were synchronised with a common start trigger so that the data from both systems could be aligned for the post processing analysis.
Targets were placed on the lateral surface of the glove near the junction between the hand and wrist to digitise its motion and calculate impact velocity. Additional targets were attached to the head and the spine of the Hybrid III to measure the overall kinematics of the dummy during impact. Images were captured with a Kodak HG2000 high speed video camera. The camera recorded the event at 1000 images per second. Digitisation of the data was completed using Image Express for video recording and processed according to SAE J211/2.34
After an appropriate warm up period, the boxer was asked to lightly punch the head of the instrumented dummy with their wrapped and gloved hand. If there was no pain or discomfort, they were asked to increase their punch strength until they reached a point where they were throwing “normal” punches. Once the boxer was comfortable throwing punches, they were instructed to strike the instrumented dummy three times with a straight punch to the jaw region with their dominant hand. Figure 3 shows the orientation of the punch. Both left and right handed boxers participated in this study. In the final analysis there was no way of determining if the boxer was delivering his maximal punch force, but the competition that developed between the boxers led the researchers to believe that they were delivering punches with maximal effort.
Rotational acceleration of the headform was calculated using the 2D in-line method.31,32 This method takes into account differences in acceleration measured on a rigid body to derive rotational acceleration about an axis. Translational accelerations (x and z direction) were measured to calculate the rotational acceleration about the y axis. Axis transformation equations were used to transfer the measured and calculated accelerations to the headform cg.
Figure 4 shows the x direction equilibrium of forces during a boxer’s punch. The equilibrium was determined using the upper neck shear load and headform x direction cg acceleration. Summing forces in the x direction gives the mass times head acceleration:
where Fpx is the force applied to the headform by the boxer in the x direction, Fnx is the neck shear force, m is the mass of the headform, and Ax is the x direction translation acceleration at the headform cg. This relationship assumes the motion of the headform does not substantially alter the orientation of the head and neck, so the lateral and vertical responses become important. These effects occur later in the impact. By measuring the neck shear force and head acceleration, the punch force can be determined from eq 2.
The effective mass of the boxer’s punch was determined using the momentum equation:
where mh is the effective mass of the puncher’s hand, Vp is the punch velocity, Vh is the Hybrid III head velocity, and m is the mass of the Hybrid III head. The Hybrid III head weighs 3.64 kg for components above the upper neck load cell. This equation neglects the portion of the neck and torso involved in the impact. These masses are influential later in the impact sequence. The peak velocity of the boxer’s hand was derived from integration of the resultant hand acceleration. This value was confirmed by differentiation of the hand motion collected by high speed video. Translational head velocity was calculated by integrating the translational acceleration of the head cg.
Energy transfer to the head was determined by multiplying the Hybrid III head mass by the square of the head velocity determined by integration of the x direction acceleration of the Hybrid III head. The punch power was determined as the time varying product of the punch force times the Hybrid III head velocity.
Thirty punches from 10 boxers were analysed. The punches were classified as either straight, extended, or glancing blows. Ten of the 30 punches (33%) were classified as a straight punch defined as a punch where the wrist remained rigid and straight. Eight (27%) were extended punches where the wrist dorsiflexed shortly after impact. The third impact condition was the glancing blow where the blow did not land cleanly but rolled off the headform to the right or left (40%). The glancing blows were neglected in the analysis. In the final analysis, 18 punches from four weight classes and seven boxers (flyweight, light welterweight, middleweight, and super heavyweight) were used.
Table 1 shows that the peak punch force ranged from 1990 to 4741 N. The mean force for all 18 landed punches was 3427 (SD 811) N. Using the one way ANOVA procedure, an analysis of variance for punch force was conducted for wrist position. There was no significant difference (p = 0.16) between the rigid wrist (3671 (SD 814) N) and the flexed wrist (2775 (SD 780) N) in terms of mean punch force. Peak punch force and weight class were compared using the same procedure. There was a significant difference (p = 0.021) in punch force between the various weight classes. The relationship between force and weight was determined by Pearson’s correlation, which measures the association between variables. Table 2 shows that punch force increases linearly with weight class (r = 0.539, p = 0.021).
The Tekscan system was used to measure the portion of the impact force delivered to the jaw region, which was defined as the bottom third of the faceform. The study set out to determine the amount of force entering the base of the skull through the mandible. Tekscan was used because in many instances the punch was centred higher on the headform and covered a greater area, so the total force would not represent the force delivered to just the jaw region. The mean force applied to the mandible for all landed punches was 876 (SD 288) N (table 1). There was no significant difference (p = 0.077) between force applied to the jaw with the rigid (984 (SD 300) N) and the flexed wrist (616 (SD 199) N).
The Hybrid III neck absorbed a portion of the overall punch force. The mean neck shear force for all 18 landed punches was 994 (SD 318) N (table 1). There was no significant difference (p = 0.100) between neck shear force with the rigid (1105 (SD 386) N) and the flexed wrist (761 (SD 166) N). Nij (overall neck injury risk value) averaged 0.27 (SD 0.07) and was well below the current tolerance level of Nij = 1.0.
The hand velocity was determined by integrating the resultant hand acceleration up to face contact. The mean hand velocity at impact was 9.14 (SD 2.06) m/s (table 1). The glove motion was tracked using video analysis to verify the accuracy of the integrated hand velocity targets. The position-time traces generated from the analysis were differentiated to obtain hand velocity. The results showed good correlation between the two velocity measurements. Data relating to hand velocity were analysed using integrated hand acceleration.
ANOVA analysis determined that there was a significant difference (p = 0.019) between the rigid wrist (8.16 (SD 1.38) m/s) and the flexed wrist (9.21 (SD 2.17) m/s) in terms of hand velocity. Comparisons between hand velocity and weight class showed no significant difference, but the association between the two variables is not linear (r = −0.071, p = 0.779; table 2).
The mean effective mass for all landed punches was 2.86 (SD 2.03) kg. No significant differences were seen between rigid and flexed wrist groups (p = 0.271). The analysis also showed the effective mass varies with translational force (p = 0.012) but only has a slight linear association with the weight of the boxer (r = 0.484, p = 0.042).
The resultant translational headform acceleration was determined using the calculated accelerations from the 2D in-line method. Prior to calculating the rotational accelerations, the translational acceleration traces were pre-processed using a 1650 Hz Butterworth filter specified in the SAE J211 standard. The mean head acceleration for the headform cg was 58 (SD 13) g with average duration of 11.4 ms. The analysis of variance determined that there was no significant difference (p = 0.135) between the rigid wrist and the flexed wrist with respect to translational acceleration of the headform.
Comparisons between acceleration and weight class were made using the same procedure. Pearson’s correlation coefficient was calculated to determine the relationship between translational acceleration and boxer weight. A weak linear relationship existed between the two variables (r = 0.432, p = 0.074). The final translational velocity of the head was determined by integrating the head cg acceleration in the x direction. The average ΔV (change in head velocity) of the head was 2.97 (SD 0.81) m/s. Comparisons between head velocity and weight class showed a significant difference (p = 0.017) with respect to the boxer’s weight (table 1) and the association between the two variables was linear (r = 0.555, p = 0.085, table 2).
The rotational acceleration of the headform about the y axis was calculated using the 2D in-line method. The mean rotational head acceleration for all landed punches was 6343 (SD 1789) rad/s2 (table 1). The analysis of variance determined that there was no significant difference (p = 0.423) between the rigid wrist and the flexed wrist with respect to rotational acceleration of the headform (table 1). However, a linear relationship existed between weight and rotational acceleration (r = 0.524, p = 0.026; table 2).
Statistical analysis of the injury parameter HIC showed that there was a significant difference (p = 0.071) between the rigid wrist (90 (SD 53)) and the flexed wrist (43 (SD 37)) groups. The mean HIC from all punches was 71 (SD 49). The ANOVA revealed that HIC varies with weight class (p = 0.002). The Pearson correlation test determined that the relationship is approximately linear (r = 0.672, p = 0.002) with respect to the boxer’s weight. Using the same procedure, it was determined that HIC also has a linear relationship with punch force (r = 0.886, p = 0.000).
Figure 5 shows the kinematics of a flyweight boxer where the wrist remained rigid (us018-3). Initial contact of the glove with the headform was signalled by an event trigger attached to the headform (fig 5, line and photo A). Peak rotational (5209 rad/s2) and translational (68 g) acceleration occurred approximately 3 and 4 ms, respectively, after initial contact (fig 5, lines and photos B and C). The total duration of impact was 13 ms. The peak force of 3737 N occurred 5 ms after initial contact. The peak force applied to the jaw was 1047 N and the peak neck shear force was 946 N. Both occurred with the peak punch force. The effective mass for this punch was 4.4 kg with a corresponding hand velocity of 6.7 m/s. The calculated HIC was 101. The end of the blow occurred at line E in the graph, when the fist contacted the chest of the Hybrid III dummy.
The energy transfer from all punches averaged 17.2 (SD 8.9) J (or Nm). The punch power averaged 6574 (SD 3453) W (or J/s). Both values increased when only the rigid wrist tests were averaged to 20.2 (SD 8.9) J energy transfer and 8014 (SD 3724) W punch power.
The current study contributes new information about head injury risks by combining data on the head impact response of a biofidelic dummy and the measured punch force of Olympic class boxers throwing straight punches. The head-neck assembly of the Hybrid III headform closely represents the mass and compliance of the average human head and neck. With this system, both the risk of injury in terms of HIC, and translational and rotational acceleration were explored as regards the momentum transfer by a punch to a biofidelic Hybrid III surrogate.
The average peak force was 2625 (SD 543) N for the middleweight boxer and 4345 (SD 280) N for the super heavyweights. This is consistent with the peak forces reported in the literature which range from 1666 to 6860 N.3,6–8 The boxers’ hand velocity was 9.14 (SD 2.06) m/s and had no significant correlation with the weight of the boxer, peak force, or severity of the punch. This level is lower than that determined by Smith and Hamill5 and may reflect the fact that these tests only involved straight punches. A later study by Smith et al7 found peak punch forces in the range of values reported here.
Statistical analysis of the injury parameter HIC showed there was a significant difference (p = 0.06) between the rigid wrist (90 (SD 51)) and the flexed wrist (48 (SD 37)) groups. The mean HIC from all punches was 71 (SD 48). The ANOVA revealed that HIC varies with weight class (p = 0.0005). The Pearson correlation test determined that the relationship is approximately linear (r = 0.672, p = 0.002) with respect to the boxer’s weight. The same procedure showed that HIC also has a linear relationship with punch force (r = 0.889, p = 0.000). These findings indicate that the differences in HICs are better explained by changes in the punch force generated than by the weight of the boxer. However, the relationship between wrist position and risk of injury is unknown at this time. In both positions, the risk of injury predicted by HIC is less than 2% implying a very low risk of injury from translational acceleration.
High rotational accelerations were calculated. The average peak rotational acceleration was 6343 (SD 1789) rad/s2. Ommaya et al17 indicated a rotational acceleration of approximately 4500 rad/s2 was required to produce concussion. They also stated that severe DAI occurred at 18 000 rad/s2, and moderate and mild DAI at 15 500 and 12 500 rad/s2, respectively. Earlier studies by Pincemaille et al35 measured rotational accelerations of 13 600 krad/s2 and rotational velocities of 48 rad/s during boxing; no cases of concussion were reported. The current tests with the Hybrid III also show high rotational accelerations, however, the data reflect a higher tolerance than those specified by Ommaya et al17 and Gennerelli et al.25 Since their tolerances are based on scaling of animal data, a question may be raised about the adequacy of the technique, which assumes similar geometry and equivalent material characteristics between animal and man.
In recent studies of concussion in professional football, Pellman et al21,22 reproduced game impacts with Hybrid III dummies and found concussion occurred at average peak rotational accelerations of 6596 (SD 1866) rad/s2. These levels are consistent with the rotational accelerations of 6343 (SD 1789) rad/s2 found in this study. However, the strongest correlations with concussion were with translational acceleration and head ΔV. No concussions were found with peak accelerations of 68 (SD 15) g and HIC of 143 (SD 37). These levels are above that determined for the boxing punch. In the NFL, concussion occurred with head accelerations of 94 (SD 28) g and HIC of 345 (SD 181). These levels are well above those delivered by the Olympic boxers to the Hybrid III dummy. Based on this work, it can be hypothesised that rotational acceleration is an important factor in boxing injuries. Using the injury risk functions given in Pellman et al,21 the risk of concussion for the boxer punches averaged 13% (SD 10%) for HIC, 20% (SD 2%) for translational head acceleration, and 3% (SD 0%) for head ΔV, but 68% (SD 3%) for rotational acceleration.
Rotational acceleration had good linear correlation with weight class. Weight class also showed good correlation with punch force, jaw force, HIC, and head velocity. These results support previous epidemiological studies showing that head injuries occur more frequently in the heavier weight classes36 and the general mechanics of the boxing punch.37 While weight was a good predictor, punch force had a stronger correlation with HIC, rotational acceleration, and head velocity. In addition, punch force also correlated with translational acceleration. Hand velocity did not seem to affect the severity of impact. This means the effective mass of the boxer’s punch is more important in increasing the severity of a blow.
The effective mass from the punch was determined using conservation of momentum. The effective mass for all boxers analysed averaged 2.9 (SD 2.0) kg. The effective mass for the flyweights was 2.3 (SD 1.1) kg, for the light welterweights 2.7 (SD 1.1) kg, for the middleweight 0.8 (SD 0.2) m/s, and for the super heavyweights 5.0 (SD 2.4) kg. The effective mass calculated for the flyweights, light welterweights, and the super heavyweights approximated the effective mass of their arms.38 The only middleweight boxer evaluated had a smaller calculated effective mass due to the boxer dorsal flexing his wrist upon impact. In this case, the effective mass was approximately the mass of the middleweight boxer’s hand.
Previous research has determined the impact force of a boxer’s punch.
This study uses a humanlike, frangible dummy face to determine the punch force to the jaw and head dynamics for Olympic level boxers.
Flexing the wrist decreases the effective mass behind the punch and decreases the risk of injury as estimated by HIC. HICs are higher for the rigid wrist because of the increased duration and the effective mass of the impact. When the wrist flexes, the palm of the hand contacts the chest decreasing the duration of the pulse by 10%. In either scenario, HICs are low for straight blows to the chin with a risk of severe traumatic brain injury of <2%.20 The average HIC in the current study was 71 (SD 49), well below the proposed NFL concussion threshold of 250.21,22
While the number of punches collected and analysed in this project was low, the results could help define the acceleration conditions under which MTBI occurs in boxing. There are also possibilities for developing useful training methods to teach boxers techniques to increase the effective mass of their punch. The described methodology of measuring translational and rotational accelerations could be applied to the regular evaluation of punch strength and severity for other punches such as the jab, hook, and uppercut. Data from this study may be useful in developing standards for evaluation of boxing gloves and head gear.
The complexity of quantifying the severity of presumably knockout punches to the jaw region by Olympic class boxers is illustrated by the head acceleration measurements. Low HIC measurements indicate that translational acceleration of the head in this study had only a marginal influence on the severity of a boxer’s punch. In contrast, rotational accelerations appeared to be the dominate response within the study and were consistent with levels found in concussion of professional football players.21,22 However, additional research is required to clarify the importance of translational and rotational acceleration of the head for the severity of a punch delivered to the jaw region. The cumulative effect of multiple punches landed on the head was not addressed in this study. The acceleration levels reported in this study may be sufficient to cause some level of head injury if multiple punches are landed. Thus, analysis of additional parameters not collected in this study may be necessary to fully understand the injury mechanism of the knockout punch.
Force delivered to the jaw does not seem to be a good predictor of either HIC or rotational acceleration. It is uncertain, at this time, how force applied to the jaw is related to risk of MTBI or if the Hybrid III dummy in its current form has sufficient biofidelity to measure this parameter accurately. Hybrid III headforms with a deformable face insert such as that developed by Melvin et al,26 offer a state of the art test device. However, a headform with an articulating jaw may be needed to further explore head response to blows to the jaw region. While the peak jaw load represents only 26% of the impact force, it is consistent with the small region of the face instrumented with Tekscan.
The authors would like to acknowledge all the athletes who took time from their busy training schedules to participate in our study. We would also like to thank Capt. Wilson for allowing us to conduct our research in the boxing gym at Camp Lejeune, North Carolina, Dr Marilyn Boitano, and the staff of the United States Amateur Boxing Association for their commitment to safety. Special thanks are extended to Don Sherman whose involvement was essential for the success of the project.
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