How older people move in bed when they are ill

Kenneth Rockwood MD, FRCPC, FRCP is Professor of Medicine (Geriatric Medicine & Neurology) at Dalhousie University, and a staff physician at the Halifax Infirmary of the Nova Scotia Health Authority. He tweet @Krockdoc  

“The dangers of going to bed”, elaborated by Richard Asher in 1947 illustrates for just how long the hospital bed has been recognized as a hazard for older adults.  It can also be source of rich clinical information.  Understanding this through quantification and plain language descriptors offers one means to “geriatrize” routine care. Like many of such workaday skills, assessing how someone moves in bed is not that tricky, but it requires both the cognitive task of paying attention and the affective one of wanting to do so.

Here is what clinicians experienced in the ways of acutely ill older people understand about how patients move in bed. To begin, there appears to be a hierarchy.  For example anyone who needs help to sit up cannot swing their legs over the side of the bed unaided.  Slightly less obviously, most people find it easier to pull to sit up (a steadying hand, a split bedrail) than to push themselves into a seated position. At a large scale, that there should be a hierarchy is obvious: when people are well they move about unaided, as they become ill, they move much less.  When they are about to die, typically they hardly move at all.  Each of those states requires clinical actions. For example, the person who cannot move off their pressure points might require a lot of Teflon: endotracheal intubation, intravenous fluids, a Foley catheter.

We think of all this in an ordinary way as ‘clinical common sense’.  Even though it can be usefully quantifed, few mobility instruments differentiate between grades of movement of people who cannot get out of bed without help. An exception is the Hierarchical Assessment of Balance and Mobility, introduced in a 1995 Age and Ageing paper.  Understanding how the hierarchy operates has been advanced recently by work from Oliver Hatheway. Then a medical student (now a newly minted doctor) he put his undergraduate physics and mathematics degree to good use.  In a new paper in Age and Ageing he took another look at data from a 2011 observational study of changing mobility and balance in older adults admitted to general or geriatric medical services.  Using the model of a damped pendulum to capture the dynamics of moving in bed, Dr. Hatheway demonstrated several results, two of which especially should be highlighted.

First, a lot of prognostic information is packed into the first initial 48 hours following hospital admission. Few people die in hospital whose mobility improves during this crucial period.  In contrast, most people (75%) die if their mobility gets worse.  Second, frailer patients are more likely to show early decline than are fitter patients.  Similarly, the fittest patients are those most likely to respond.  This is the so-called “Matthew Effect” (“to they who have much more shall be given”) or popularly, “the rich get richer”.  The converse: “even that which they have shall be taken away”, is an unsparingly accurate summary of the risk of acute illness (and its treatment) to frail patients.

Why does this matter?  Of many reasons, let me highlight a few.  Most important, it allows us to develop an action plan to improve care.  By drawing to attention the adverse nature of decline in bed mobility, we can motivate its assessment and reporting.  As part of this, quantifying how mobility changes can facilitate trials of interventions to improve bed mobility as a means of reducing its adverse consequences. Second, knowing its value should motivate routine, passive electronic measurement of bed mobility. This requires algorithms, which in turn requires data, all of which is non-trivial.  In my view, we must automate. In contemporary acute hospital wards, adding even a single clinical measure is the path to inaction.  In contrast, for better or for worse, we are very good at reacting to numbers, as “troponitis” made clear.

Third, looking at how people move in bed aids – and democratizes – prognosis. It’s not just that dynamic views offer better information than do snapshots. For a patient or family for whom the prospect of imminent death is overwhelmingly difficult, having an extra 48 hours to prepare, to call family members, to think about spiritual counselling, and to retain a strand of hope can each be of determining value.  Crucially too offering the opportunity to assess the treatment response can bridge between the common false dichotomy of choosing between “doing everything” and “doing nothing”.  It is easy to show families what to look for, and thereby empower them to dispute what they are being told, or help them to accept what we see.

Understanding the underlying science of what we do is essential to advancing geriatric medicine.  This type of modelling allows us to delve deeper without falling prey to complete reductionism.  For many of our most crucial clinical questions – being those that revolve around how complex systems fail and how they can be restored – the basic science of geriatric medicine is mathematics.  By collaborating with mathematicians, bioinformaticians, computer scientists and engineers, we can codify the “common sense” of geriatric medicine.  In so doing, we can develop tools and ways of thinking that can help us as catalysts.  There will not be enough acute geriatric teams so that they alone can be charged with medical care of frail older adults.  The goal should be to “geriatrize” how we provide health care if we are to do right by our ageing population.  This is a basic science that still allows us to consider the whole person.