Elsevier

Sleep Medicine

Volume 14, Issue 12, December 2013, Pages 1364-1368
Sleep Medicine

Original Article
Seasonal trends in restless legs symptomatology: evidence from Internet search query data

https://doi.org/10.1016/j.sleep.2013.06.016Get rights and content

Highlights

  • We examined Internet search query data to look for seasonality in restless legs.

  • Cosinor analysis was applied to data obtained from Google Trends from 2004 to 2012.

  • Search queries demonstrated seasonality with more searches in summer than in winter.

  • Seasonal patterns were consistent across time and geographic locations.

Abstract

Objective

Patients with Willis-Ekbom disease (restless legs syndrome [RLS]) frequently report seasonal worsening of their symptoms; however, seasonal patterns in this disorder have not been systematically evaluated. The purpose of our investigation was to utilize Internet search query data to test the hypothesis that restless legs symptoms vary by season, with worsening in the summer months.

Methods

Internet search query data were obtained from Google Trends. Monthly normalized search volume was determined for the term restless legs between January 2004 and December 2012. Using cosinor analysis, seasonal effects were tested for data from the United States, Australia, Germany, the United Kingdom, and Canada.

Results

Cosinor analysis revealed statistically significant seasonal effects on search queries in the United States (P = .005), Australia (P = .00007), Germany (P = .00009), and the United Kingdom (P = .003), though a trend was present in the search data from Canada (P = .098). Search queries peaked in summer months in both northern (June and July) and southern (January) hemispheres. Search query volume increased by 24–40% during summer relative to winter months across all evaluated countries.

Conclusions

Evidence from Internet search queries across a wide range of dates and geographic areas suggested a seasonality of restless legs symptomatology with a peak in summer months. Our novel finding in RLS epidemiology needs to be confirmed in additional samples, and underlying mechanisms must be elucidated.

Introduction

Willis-Ekbom disease (restless legs syndrome [RLS]) is a sensorimotor disorder characterized by the cardinal symptom of a compelling urge to move the extremities. These sensations emerge at rest, are relieved (at least partially or transiently) by movement, and exclusively or more prominently occur at night [1]. RLS is a common malady, with 1.9–4.6% of the population afflicted with the disorder [2]. RLS has been associated with considerable daytime consequences, including decreased quality of life, insomnia, daytime sleepiness, and impaired mood or concentration [3], [4]. In addition, a burgeoning literature suggests possible associations of RLS with cardiovascular sequelae, highlighting the importance of research regarding the underlying pathophysiology and epidemiology of the disorder [5].

Seasonal variation in symptomatology has previously been suggested as a phenotypic pattern within RLS [6]. In support of this contention, previous reports of patients with both primary and secondary RLS have noted symptoms that were more frequent or intense during the summer [7], [8], [9]. Another study noted that patients with RLS often complained that a hot environment (e.g., summer season, blanket on the legs) aggravated their symptoms [10]. However, worsening of symptoms in the summer is not universal, as previous reports have conversely described worsening of RLS symptoms in the winter in some patients [11]. Despite these clinical observations, seasonal variation in RLS symptoms has not been systematically examined and represents an important gap in RLS epidemiology.

The development of the Internet and search engines has quickly made a vast amount of information easily accessible to the public and has opened a new avenue for epidemiologic research. Approximately 4.5% of all Internet searches are for health-related information [12]. Recently Google has made their search query data available to the public through the Google Trends tool (google.com/trends), which represents a large repository of search query data from around the globe since 2004. Previous studies have leveraged these data to study seasonal or other time-varying patterns of several health conditions, including influenza [13], depression [14], smoking habits [15], major mental illness [16], and health-related behavior changes [17].

The purpose of our investigation was to utilize Internet search query data to test the hypothesis that there is seasonal variability to symptoms of RLS. Our hypothesis was that there would be a seasonal pattern to restless legs symptomatology, with relative worsening during summer months.

Section snippets

Query selection and data collection

Google Trends is a Web-based tool that analyzes a portion of all Google Web site searches. This process is described in detail elsewhere (support.google.com/trends) and is summarized below. For a given search term, Google Trends computes how many searches have been done relative to the total number of searches done on Google in an effort to provide the likelihood that a random user will enter a certain search term at a particular physical location and time. The system automatically eliminates

Results

Results of both primary and secondary analyses are presented in Fig. 1. Visual inspection of the search query data for the United States and Australia revealed definite peaks and troughs. Cosinor models confirmed this finding, with statistically significant seasonal effects found for restless legs in the United States (A, 4.9; P, 7.4) (P = .005) and Australia (A, 7.9; P, 1.3) (P = .00007). Notably, the peak for both countries was in the summer (mid-July for the United States; mid-January for

Discussion

Although previous case reports have suggested seasonal patterns in RLS, no previous studies to our knowledge have systematically examined this phenomenon. The purpose of our study was to elucidate seasonal variation in restless legs symptoms using Internet search query data. Both our primary and secondary analyses demonstrated peaks in search volume queries during the summer months (June and July) in northern hemispheric countries, with a peak in January in a southern hemispheric country, which

Funding sources

Dr. Plante is supported by unrelated research grants from the American Sleep Medicine Foundation, Brain and Behavior Research Foundation, and the National Institute of Mental Health (K23MH099234).

Conflict of interest

The ICMJE Uniform Disclosure Form for Potential Conflicts of Interest associated with this article can be viewed by clicking on the following link: http://dx.doi.org/10.1016/j.sleep.2013.06.016.

. ICMJE Form for Disclosure of Potential Conflicts of Interest form.

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