Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions

Abstract

Osteoarthritis (OA) is a chronic condition often associated with pain, affecting approximately fourteen percent of the population, and increasing in prevalence. A globally aging population have made treating OA-associated pain as well as maintaining mobility and activity a public health priority. OA affects all mammals, and the use of spontaneous animal models is one promising approach for improving translational pain research and the development of effective treatment strategies. Accelerometers are a common tool for collecting high-frequency activity data on animals to study the effects of treatment on pain related activity patterns. There has recently been increasing interest in their use to understand treatment effects in human pain conditions. However, activity patterns vary widely across subjects; furthermore, the effects of treatment may manifest in higher or lower activity counts or in subtler ways like changes in the frequency of certain types of activities. We use a zero inflated Poisson hidden semi-Markov model to characterize activity patterns and subsequently derive estimators of the treatment effect in terms of changes in activity levels or frequency of activity type. We demonstrate the application of our model, and its advance over traditional analysis methods, using data from a naturally occurring feline OA-associated pain model.

Figure 1

Left: Sample daily accelerometer data for a typical cat in the study of the treatment effect of meloxicam, showing the crepuscular pattern of activity expected in cats. Right: Histogram showing the proportions of time spent in different levels of activity during a 24 h period.

Figure 2

Plots of the mean activity count over each period for each subject separated by treatment group. The dark line in each panel is the group mean activity count.

Figure 3

Mean activity count for each subject in the placebo and meloxicam treatment period for latent state 4–6. The dark line in each panel is the overall mean activity count. The mean increase in the activity count is 23.0, with a standard error of 7.2.

Figure 4

Estimated quantiles based on fitted model versus observed quantiles. In both distributions, from the 1st up to the 75th quantiles are zero. The rest of the quantiles also match well with each other, which indicates there is no lack of fit in the proposed model.