Despite the clear clinical need to sleep well, not long ago, sleeping only a few hours and at least
giving the appearance of still being productive was a badge of honor. It was suggestive of the
impenetrable invincibility that we think we should display. No more. At least in many
circles, it’s not only about how many hours of sleep the person got the night before, but
also the quality of their sleep cycle, thanks of course to our devices. Apple Watch and Fitbit
both measure sleep, not to mention dozens of apps proclaiming to do the same. Sleep has
become something exciting for us to measure. If you look on Kickstarter you are sure to
find a new gadget proclaiming to have the answer. It has become another blinking light for us to
swipe to be incorporated into our endless data stream.
Using devices to change health related behavior is hardly new. Sporting a pedometer on your
belt like the pager (remember those) of an ER doctor was a public declaration that you are
taking your health seriously. The research to support the use of these devices is promising.
After all, much of this technology includes elements of already empirically supported
interventions (e.g. goal setting, self monitoring, feedback, rewards, etc..) However, Sullivan and
Lachman (2017) found that challenges remain, especially as it relates to encountering
obstacles, action planning, and modifying environmental factors. But modifying the
environment, in part, is exactly what individuals need to do in order to improve the quality of
Improving sleep patterns often relies on altering our behaviors and habits. The National Sleep
Foundation outlines 7 straightforward behaviors to promote in order to optimize sleep. There
are the obvious such as caffeine reduction/elimination to perhaps the more subtle, like altering
your sleep environment. But if the research found by Sullivan and Lachman (2017) stands, this
will present as a significant challenge regarding translating sleep data to behavior. Unlike other health related data, the information we receive from sleep data is secondary.
To improve sleep it may require changing behaviors or making environmental alterations the
following evening. Alternatively, if an individual is interested in improving how many steps they
would take in a day, they may simply walk more to meet their goal. This begs the question, how
does receiving sleep data translate to behavioral change? Ravichandram and others (2017)
also looked at this question when they investigated the usefulness of sleep sensors in
promoting healthy sleep patterns. They found, in part, that the method in which data is given
back to the user, can often be misleading, which may lead others to develop a “broken mental model” about their sleep and may not necessarily develop into positive behavioral changes.
While of course the transition of the unrealistic, invisible individual who does believe they do not
require sleep to the informed “sleep medicine expert” is one that should be celebrated, this also
poses the concern that an individual “thinks they know” and makes (or doesn’t make) behavioral
changes that may be erroneous. The increased awareness of sleep and how it impacts our
daily lives is a positive sign. However, more research is still needed to determine how this kind
of data can carry over to meaningful behavioral change.