Complex technology can bog down research into sleep issues in people on the spectrum
The evidence to date points to profound differences between the sleep of individuals with autism and that of typical people or those with developmental delay. But our ability to identify and treat sleep problems is hampered by expensive and complex technology. This technology is particularly difficult to use with individuals who, by virtue of their condition, are wary of new people, devices or circumstances.
We use a method called polysomnography to assess brain function during sleep. In this technique, participants have to sleep wearing an electroencephalography (EEG) cap that has multiple wires emerging from it, as well as a sensor over their mouth and nose. At the moment, polysomnography produces the most comprehensive assessment of sleep of any method, providing information on the stages of sleep as well as the presence of sleep disorders. There’s a range of sleep features that may be impaired in autism, from reduced rapid eye movement (dream) sleep to increased levels of sleep-disordered breathing. We need to define which of these problems a child has. This information can inform treatment.
Yet children with autism require quite a long time to get accustomed to the polysomnography. Sometimes in our work, two or three researchers spend many hours over the course of a month with the children, helping them get used to the device. They talk to them and to their parents about how the apparatus works. They let the children try the device on their dolls or watch the sleep study being implemented on their siblings. This takes a lot of time and resources, which can restrict our ability to do this kind of study in sufficiently large numbers of individuals to characterize sleep in autism.
New technologies may help us. For example, we are looking into a stand-alone device that sits beside the bed and uses an acoustic system to study sleep parameters. The device picks up the sound of a person’s heartbeat to measure her heart rate and monitors her sleep patterns from the sound of her breathing. We are comparing the data we get from this device with those from polysomnography to see whether we can use it to assess sleep dysregulation and sleep architecture in a large number of individuals with autism.
One issue that technology cannot solve, however, is bias. If I advertise for a sleep study in autism, the people who show up at my door are those who have profound sleep problems. We don’t see people with milder problems and so may not be able to ferret out the diversity of sleep problems in autism.