  Physical activity is disrupted in many psychiatric disorders. Advances in
everyday technologies (e.g. accelerometers in smart phones) opens exciting
possibilities for non-intrusive acquisition of activity data. Successful
exploitation of this opportunity requires the validation of analytical methods
that can capture the full movement spectrum. The study aim was to demonstrate
an analytical approach to characterise accelerometer-derived activity patterns.
Here, we use statistical methods to characterise accelerometer-derived activity
patterns from a heterogeneous sample of 99 community-based adults with mental
illnesses. Diagnoses were screened using the Mini international
Neuropsychiatric Interview, and participants wore accelerometers for one week.
We studies the relative ability of simple (exponential), complex
(heavy-tailed), and composite models to explain patterns of activity and
inactivity. Activity during wakefulness was a composite of brief random
(exponential) movements and complex (heavy-tailed) processes, whereas movement
during sleep lacked the heavy-tailed component. In contrast, inactivity
followed a heavy-tailed process, lacking the random component. Activity
patterns differed in nature between those with a diagnosis of bipolar disorder
and a primary psychotic disorder. These results show the potential of complex
models to quntify the rich nature of human movement captured by accelerometry
during wake and sleep, and the interaction with diagnosis and health.
