%0 Journal Article
%@ 2369-6893
%I JMIR Publications
%V 2
%N 1
%P e7
%T Sleep Phenotypes in Chronic Pain Sufferers: Application of Machine Learning to a Large Database
%A Kong,Xuan
%A Ferree,Thomas C
%A Gozani,Shai N
%+ NeuroMetrix Inc., 1000 Winter Street, Waltham, MA, 02451, United States, 1 781 314 2722, xkong@neurometrix.com
%K sleep phenotype
%K chronic pain
%K machine learning
%K clustering analysis
%D 2016
%7 12.12.2016
%9 Poster
%J iproc
%G English
%X Background: Chronic pain affects over 100 million American adults. There is a negative reciprocal relationship between chronic pain and sleep. As many as 80% of chronic pain patients report poor sleep quality and daytime fatigue. We have recently reported on the clinical benefits of fixed-site high-frequency transcutaneous electrical nerve stimulation (Quell, NeuroMetrix, Inc) in a chronic pain cohort. In addition to delivering therapeutic neurostimulation, this device collects health data including utilization, sleep measures, and activity metrics. The data is communicated to the patient through a smartphone app and aggregated in a cloud server. This digital health database presents a novel opportunity to study population characteristics in a large cohort of chronic pain sufferers. Objective: Our primary objective was to use machine learning techniques on a large database of sleep data in chronic pain sufferers to determine “sleep phenotypes.” The long-term goal of this research is to develop personalized therapeutic profiles that optimize sleep in chronic pain patients. Methods: De-identified data from device users consenting to have their data uploaded to a cloud server was analyzed. Individual users were characterized by their median sleep data. The analyzed sleep parameters included total sleep time (TST, hours), sleep efficiency (SE, %), periodic leg movement index (PLMI, events/hour), position change rate (PCR, events/hour), and time out of bed (OOB, minutes). K-means clustering was used to partition the data set into 3 mutually exclusive clusters based on TST, PLMI, PCR, and OOB. The optimal number of clusters was determined by the Silhouette value. Clustering was based on the correlation metric. One-way ANOVA was used to test whether the 3 cluster groups had a common mean for each sleep parameter. For parameters with differences in group means, t test was used to identify which pairs of means were different. Results: A total of 389 users with 5 or more nights of TST between 4 and 12 hours were included in the analysis. The sizes of the 3 clusters were 161 (41.4%), 147 (37.8%), and 81. None of the sleep parameters had the same mean among three clusters (P<.001). The 3 clusters represented 3 sleep phenotypes. The largest group (n=161) was a “good sleeper” phenotype characterized by a mean TST of 7.3, SE of 95.2, and low PLMI (2.1), PCR (1.3), and OOB (1.4). The second largest cluster was a “moderate sleeper” phenotype characterized by a mean TST of 7.4, SE of 92.4, low PLMI (3.9) and PCR (0.9), but relatively high OOB of 12.7. The third cluster was a “poor sleeper” phenotype characterized by TST of 6.6, SE of 91.2, and a high PLMI of 11.7. All pair-wise cluster means were different (P<.025), except for TST between good and moderate sleepers (P=.452). Conclusions: We identified 3 sleep phenotypes in a large cohort of chronic pain sufferers. The phenotypes reflected a progression from good to poor sleepers. The poorer sleepers were characterized by either a large amount of time out of bed during the night or a high rate of periodic leg movements. 
%R 10.2196/iproc.6103
%U http://www.iproc.org/2016/1/e7/
%U https://doi.org/10.2196/iproc.6103