There may be a relationship between sleep and pain in patients with chronic back 
pain. We collected day-time pain and nighttime activity data from 18 patients 
diagnosed with chronic back pain. The patients were followed for 6 days and 5 
nights. Pain levels were collected every 90 min between 0800 hours and 2,200 
hours using a computerized electronic diary. Activity levels were collected 
using a wrist accelerometer (Actiwatch AW-64). The Actiwatch sampled activity 
counts every 1 min. Patients were asked to wear the Actiwatch on their 
non-dominant arm. The pain level measurements were interpolated using cubic 
splines. A mean pain level was calculated for each period 0800 hours to 2,200 
hours as well as for the 6-day period. The difference between the mean pain 
levels for the 6-day period and each 0800 hours to 2,200 hours period was 
calculated for each patient. Nighttime activity data were analyzed using the 
Actiwatch Sleep Analysis software. Correlations were calculated between the 
Actiwatch Sleep Analysis variables and the mean pain level differences for each 
patient and period. The correlation analysis was performed with SPSS 7.5. We 
were unable to show any significant relationships.A different approach to 
analyze the data was used. A Self-Organizing Map (SOM) Neural Network was 
trained using the original nighttime activity level time series from 10 randomly 
selected patients. Recall was then performed on all the activity level data. 
Correlations were calculated between the pain level variance for the 6-day 
period for each patient and the corresponding difference in the SOM output 
coordinates. The correlation was found to be r = 0.73, p < 0.01). We conclude 
that daytime pain levels are not directly correlated with sleep in the following 
night and that sleep is not directly correlated with daytime pain levels on the 
following day in this group of patients. There appears to be a correlation 
between the difference in nighttime activity levels and patterns and the daytime 
pain variance. Patients who experience large fluctuations in daytime pain levels 
also show a higher variability in their nighttime activity levels and patterns. 
Even though we were unable to show a direct relationship between daytime pain 
and sleep, it may be reasonable to assume that better pain control resulting in 
less daytime pain fluctuations can provide more stable nighttime activity levels 
and patterns in this limited group of patients. By using a neural network model, 
we were able to extract information from the nighttime activity levels even 
though a traditional statistical analysis was unsuccessful.