Introduction

Snoring is common and may indicate the presence of obstructive sleep apnea (OSA). While the totality of deleterious health effects of OSA continue to be enumerated, it is currently known that in adults OSA is associated with adverse outcomes that include impaired daytime function [1] and cardiovascular morbidity and mortality [2]. Likewise, OSA is now known to contribute to cognitive and behavioral problems [3], poor growth [4], and cardiovascular sequelae in children [5]. Given its high prevalence and known morbidity, an understanding of the underlying mechanisms and natural history of OSA is vital for the development of optimal diagnostic and treatment modalities.

While the mechanisms underlying OSA are multifactorial and may vary between pediatric and adult forms of the disorder, there has been recent interest in seasonal phenomena in sleep-disordered breathing. The majority of investigations that have examined such patterns in sleep-disordered breathing have been performed in pediatric populations. One study by Gozal and colleagues demonstrated that both snoring and OSA severity exhibited seasonal variation in children, with peaks in spring/summer and winter/spring, respectively [6]. Three additional studies have demonstrated worsening of OSA in the winter/spring relative to other time periods [7–9], with two of these studies suggesting that worsening in the winter may be more prominent in or confined to children less than 5–6 years of age [8, 9]. Although less data exists in adults, Cassol and colleagues similarly found that the severity of OSA had a seasonal pattern with a peak in the winter [10]. These groups postulated winter-related changes that would increase upper airway resistance may be causal factors, including increases in upper respiratory infections, seasonal allergies, and/or worsening of air pollution/particulates resulting from home heating that all would favor upper airway inflammation and dysfunction. Notably, all of these previous studies relied on research participants who were recruited and evaluated locally.

A novel alternative method that has recently been employed to investigate the seasonality of disease is the use of internet search engine query data. Patients are now commonly using the internet, Google in particular, as a source of medical information about their symptoms or health conditions. Indeed, Google search engine query data has recently been used to investigate several health conditions over time, including influenza [11], depression [12], smoking habits [13], major mental illness [14], health-related behavior change [15], and, in sleep medicine, restless legs symptomatology [16]. These previous investigations utilized the newly released Google Trends tool (google.com/trends), which represents a world-wide repository of search query data since the year 2004.

Therefore, the purpose of the current investigation was to leverage Google search engine query data in order to investigate the seasonality of sleep-disordered breathing. Based on the prior studies outlined above, we hypothesized that there would be a significant seasonal pattern to snoring and sleep apnea with a relative increase in searches during winter months.

Methods

Query selection and data collection

Google Trends is a web-based tool that analyzes a portion of all Google web searches. The tool is described in detail elsewhere by Google (support.google.com/trends), and in prior work from our laboratory [16]. Briefly, users may enter any search term, and Google Trends computes how many searches have been done for that term relative to the total number of searches done on Google in an effort to provide the likelihood that a random user will enter that particular search term at a physical location and time. The system automatically eliminates searches that were repeated over a short period of time from the same user. Following normalizing the data against total search volume, results are displayed on a scale from 0 to 100 with individual values over time calculated by dividing each point on the graph by the highest value and multiplying by 100.

In order to investigate the seasonality of sleep-disordered breathing, we examined the search terms “snoring” and “sleep apnea”, respectively. Searches were limited by country of origin within a 7-year time period from January 2006 to December 2012. Data from 2004 and 2005 were not included because preliminary analyses identified a large outlier in search volume for sleep apnea that was temporally coincident with news headlines that connected the premature death of the popular American Football player, Reggie White, in late December 2004 with obstructive sleep apnea [17]. In addition, we repeated the searches for children for the terms “snoring children” and “sleep apnea children” in an effort to parse out pediatric-specific effects. We chose “children” over “pediatric”, “child”, or other similar terms after preliminary analyses demonstrated that “children” generated the largest search volume results. Because the default unit of time that is outputted by Google Trends may vary (e.g., 1 week to 1 month bins) based on sparseness of the data, the current study utilized monthly data that were collected by manually highlighting each data point on a given Google Trends graph, and recording the value for offline analysis (12 points per year × 7 years per search query). All searches were performed between 3 and 10 November 2013. For record-keeping purposes, a screenshot was taken of each of these data points at the time of collection.

Analysis plan

Similar to prior investigations examining the seasonality of illness using internet search query data [12, 14, 16], the a priori primary countries of interest in this study were USA and Australia. Because one country is in the northern hemisphere and the other in the southern hemisphere, this strategy allows for evaluation of seasonality of the data, since seasonal phenomena should be out of phase by approximately 6 months between these countries.

Cosinor analysis was employed to test the hypothesis that there was significant seasonal variation in normalized search volume over time, using methods similar to prior investigations from our laboratory [16]. This method and the software used to implement it are described in detail elsewhere [18]. Briefly, cosinor analysis fits a sinusoid to an observed time series and estimates the amplitude (A, magnitude of seasonal effect), phase (P, timing of seasonal peak), and length of seasonal cycle (set at 12 for monthly data). The model assumes that the seasonal pattern is smooth and symmetric, and because the sinusoid is part of a generalized linear model, the statistical significance of any seasonal effect may be calculated. Because the seasonal component of the sinusoid is composed of both sine and cosine functions, all reported p values are the original p value multiplied by 2 in order to correct for multiple comparisons. Alpha was fixed at 0.05 for significance. Cosinor analyses were performed using the “season” package in R version 2.15.2 [18]. Additionally, similar to other investigations [19], the magnitude of seasonal effect was calculated as the percent change in search volume from winter months (USA—December, January, February, and March; Australia—June, July, August, and September) to summer months (USA—June, July, August, and September; Australia—December, January, February, and March).

This study was deemed to not constitute human subjects research as defined by 45 CFR 46.102(f) of the Health and Human Services Policy for Protection of Human Research Subjects by the Health Sciences Institutional Review Board (IRB) of the University of Wisconsin-Madison, and consistent with institutional policy on use of existing data sets, was exempt from IRB oversight.

Results

Graphical results are presented in Fig. 1. Peaks and troughs were evident upon visual inspection of the search query data for USA and Australia for both snoring and sleep apnea search terms. Cosinor models largely confirmed this with statistically significant seasonal effects found for “snoring” in the USA (A = 11.8, P = 1.5, p < 0.00001) and Australia (A = 11.5, P = 7.9, p < 0.00001). The search term “sleep apnea” also showed significant seasonality in the USA (A = 3.8, P = 3.1, p = 0.001) and showed a similar trend in Australia that did not meet significant seasonality (A = 5.5, P = 7.8, p = 0.13). The pattern also held for “snoring children” (A = 13.7, P = 3.0, p = 0.002) and “sleep apnea children” (A = 10.5, P = 2.6, p < 0.00001) within the USA; however, there was insufficient search volume for cosinor analyses of these search terms in Australia. Consistent with our a priori hypothesis of a seasonal pattern, the peak searches for both snoring and sleep apnea were approximately 6 months out of phase with each other, with peaks in the winter/early spring (January to March for USA; July for Australia).

Fig. 1
figure 1

Seasonal variation in normalized internet search queries for “snoring” in a the USA and b Australia; “sleep apnea” in c the USA and d Australia; e “snoring children” and f “sleep apnea children” in the USA. Lines indicate fitted cosinor models based on continuous data from January 2006 to December 2012. Open data points with error bars are monthly means ± standard error plotted for visual purposes. Numeric values correspond to months as follows: January = 1, February = 2, March = 3, April = 4, May = 5, June = 6, July = 7, August = 8, September = 9, October = 10, November = 11, and December = 12

Full size image

All searches demonstrated a positive seasonal increase in search volume in the winter relative to summer. In general, “snoring” [(mean, 95 % CI)—USA (30 %, 26–34 %), Australia (27 %, 21–33 %)] tended to show greater seasonal variability than “sleep apnea” [USA (5 %, 0–10 %), Australia (12 %, 0–22 %)]. A similar pattern was observed for “snoring children” (50 %, 24–77 %) and “sleep apnea children” (20 %, 11–28 %) in the USA.

Discussion

The current study examined the seasonal variability in Internet search volumes for snoring and sleep apnea. Congruent with our hypothesis, results demonstrated a significant seasonality for these searches in representative countries in the northern and southern hemispheres, with peaks in search volume during the winter/early spring and nadir in the summer. Overall, our results add to the growing body of literature suggesting that seasonality plays an important role in sleep-disordered breathing.

While the results of the current study do not inform cause-and-effect relationships, there are several plausible causes of increased snoring and OSA in the winter and early spring that merit discussion. Common risk factors for habitual snoring and sleep apnea that may be influenced by season include obesity [20], alcohol consumption [21], and tobacco use [22]. In regards to obesity, seasonal increases in body mass index have been consistently described with peak weights in winter months due to relative decreases in physical activity and increases in caloric intake [23–25]. Thus, increases in weight could plausibly account for increases in search queries for snoring and sleep apnea during winter months observed in our study. However, it is noteworthy that weight gain in the winter is unlikely to account for worsening of sleep-disordered breathing in pediatric populations, as children tend to gain weight in the summer when not attending school [26]. Second, alcohol consumption can lead to relaxation of the upper airway dilator muscles, resulting in increased upper airway resistance [21]. Seasonal patterns of alcohol consumption are variable, but tend to peak in the spring, with an additional peak in late December related to the winter holidays [27]. Finally, tobacco use has also been associated with increased of risk sleep-disordered breathing [22]. The evidence that supports smoking as a contributing factor to the seasonal patterns observed in our study is mixed. Cigarette sales (and presumed consumption) tend to decrease in the winter relative to the summer [28], which would be in the opposite direction expected if smoking were a causative factor of the observed increase in search query volume for snoring and sleep apnea in winter/early spring. However, it is noteworthy that both among smokers and nonsmokers, exposure to environmental tobacco smoke increases in the winter relative to the summer, likely due to increased numbers of people smoking indoors [29].

The above observation regarding increased exposure to environmental factors such as second-hand smoke in the winter may be particularly pertinent in pediatric populations. Prior investigations have demonstrated increased risk of snoring among children and adolescents whose parents smoke [30, 31]. Moreover, parental smoking has been linked with worsening asthma, atopy, and viral respiratory infections [32, 33]. Notably, these same factors worsen in the winter, and have been hypothesized by other investigators to contribute to the seasonal patterns of sleep-disordered breathing observed in children [8]. Thus, there are likely to be complex interactions between individual physiology, exogenous substances, and the local environment that vary by season that contribute to the aggregate findings observed in our study.

There are several potential clinical implications of our findings. First, our results suggest that, on average, it is possible that mild symptoms of sleep-disordered breathing in the winter may improve or resolve in the summer and vice versa. Second, in the context of polysomnographic interpretation, a borderline or negative study during the winter may be more reassuring than one obtained during the summer. Third, as suggested by others [6], the seasonality of snoring and OSA should be taken into account by those attempting to construct screening tools for sleep-disordered breathing, particularly for pediatric patients for whom even a slight increase in the apnea-hypopnea index may be quite clinically meaningful. Fourth, although speculative, it is noteworthy that several cardiovascular comorbidities associated with OSA including atrial fibrillation, myocardial infarction, and stroke [34, 35], have all demonstrated seasonal worsening in the winter months [36–38]. Thus, future studies that examine the connections between sleep-disordered breathing, cardiovascular disorders, and seasonal patterns of interaction between these illnesses may prove a fruitful area of investigation.

Despite the potentially important insights gleaned from the current investigation, there are inherent limitations to our study that warrant discussion. First, Google Trends data does not provide demographics or other characteristics of the user performing the search, and therefore important inter-individual differences such as age or income level that may relate to the probability of accessing web-based health information could not be assessed. Second, inherent to the use of Internet search query data in the evaluation of disease is the assumption that such queries are representative of actual disease prevalence and/or severity, the accuracy of which cannot currently be verified. Third, we utilized data from a single search engine, Google, and thus there may be a selection bias since we were only able to examine data from people who chose to use this search engine. However, this risk is mitigated by the fact that Google accounts for over two thirds of all Internet searches [39]. Fourth, one might also posit that the seasonality in increased internet queries may be secondary to seasonal vacation times allowing for increased utilization of the internet when away from school or work. However, this seems unlikely given that the search volume of a particular term is normalized against total search volume for a particular location and time. This process of normalization would presumably correct for any effect of increased time available for searching the internet. Finally, while our results support a population-level effect of season on sleep-disordered breathing, they do not preclude the possibility of no seasonal effect or even the reverse seasonal effect at the individual patient level.

In summary, Internet queries for snoring and sleep apnea exhibit seasonal variability, with worsening in winter/early spring. These results add to the growing body of evidence that suggest seasonality is an important factor in sleep-disordered breathing. Further research is indicated to determine the mechanisms of seasonal trends in sleep-disordered breathing, how these seasonal effects may impact clinical practice, and whether seasonal patterns of sleep-disordered breathing interact with associated cardiovascular disorders that have similar seasonal patterns of exacerbation.