Success in orthopedics is rarely black and white. In most cases, we’re not treating diseases or completely resolving medical conditions, like a clogged artery. With orthopedic procedures, such as joint replacement surgery, we’re typically working to reduce pain and improve function. Success is making it easier for the patient to live their everyday life.
How do you gauge this type of success? The typical approach is to measure mobility, but that’s easier said than done. Today, we rely mainly on patient-reported outcome measures (PROMs), which are patient surveys and have some inherent limitations. It can be challenging to get patients to complete these surveys, and there are a number of factors that can skew patient feedback. Patients may have difficulty recalling what they did or could do in the past or may subconsciously allow their mood or personal feelings about their physician influence their responses. In some cases, patients simply misunderstand the questions, either due to ambiguous survey wording or limited health literacy.
This all adds up to incomplete and sometimes inaccurate data. But the good news is, we’re about to take a great leap forward in measurement, thanks to wearable technology.
Wearable technology is already helping healthcare providers measure successful patient outcomes. Today, mobile health solutions like Apple HealthKit and the recent generations of FitBits, Misfits, smart watches, and other technologies can provide pulse rate, step counts, and even caloric load, giving physicians a promising lens into their patients’ physical activity. My colleague Deborah Estrin, Professor of Computer Science at Cornell Tech and co-founder of the mobile health data platform Open mHealth, believes even online activity can yield important health insights. Her research has shown that patient moods affect their smartphone usage — what sites they visit and how often — and that particular patterns may indicate mood disorders or depression.
But as useful as smartphones and basic step counters are for tracking activity, we’re still fairly limited in measuring mobility. First, these data points may not tell us the full story. For example, many women carry their phones in their purse, and when they get home, they put their purse down. They may continue to walk around, but we miss those steps, giving us an incomplete picture of their movement. Second, measures like step count can be pretty misleading as a mobility measure. For example, after knee replacement surgery, we may not see an increase in the number of steps a patient takes in a day, but that doesn’t necessarily mean mobility hasn’t improved. The patient may be doing the same things they were doing before surgery — following the same routine — but doing it with much less pain and much more quickly.
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