Search Query Volume Research Articles

Emerging Trends in Information-Seeking Behavior for Alpha-Gal Syndrome: Infodemiology Study Using Time Series and Content Analysis.

Alpha-gal syndrome is an emerging allergy characterized by an immune reaction to the carbohydrate molecule alpha-gal found in red meat. This unique food allergy is likely triggered by a tick bite. Cases of the allergy are on the rise, but prevalence estimates do not currently exist. Furthermore, varying symptoms and limited awareness of the allergy among health care providers contribute to delayed diagnosis, leading individuals to seek out their own information and potentially self-diagnose. The study aimed to (1) describe the volume and patterns of information-seeking related to alpha-gal, (2) explore correlations between alpha-gal and lone star ticks, and (3) identify specific areas of interest that individuals are searching for in relation to alpha-gal. Google Trends Supercharged-Glimpse, a new extension of Google Trends, provides estimates of the absolute volume of searches and related search queries. This extension was used to assess trends in searches for alpha-gal and lone star ticks (lone star tick, alpha gal, and meat allergy, as well as food allergy for comparison) in the United States. Time series analyses were used to examine search volume trends over time, and Spearman correlation matrices and choropleth maps were used to explore geographic and temporal correlations between alpha-gal and lone star tick searches. Content analysis was performed on related search queries to identify themes and subcategories that are of interest to information seekers. Time series analysis revealed a rapidly increasing trend in search volumes for alpha-gal beginning in 2015. After adjusting for long-term trends, seasonal trends, and media coverage, from 2015 to 2022, the predicted adjusted average annual percent change in search volume for alpha-gal was 33.78%. The estimated overall change in average search volume was 627%. In comparison, the average annual percent change was 9.23% for lone star tick, 7.34% for meat allergy, and 2.45% for food allergy during this time. Geographic analysis showed strong significant correlations between alpha-gal and lone star tick searches especially in recent years (ρ=0.80; P<.001), with primary overlap and highest search rates found in the southeastern region of the United States. Content analysis identified 10 themes of primary interest: diet, diagnosis or testing, treatment, medications or contraindications of medications, symptoms, tick related, specific sources of information and locations, general education information, alternative words for alpha-gal, and unrelated or other. The study provides insights into the changing information-seeking patterns for alpha-gal, indicating growing awareness and interest. Alpha-gal search volume is increasing at a rapid rate. Understanding specific questions and concerns can help health care providers and public health educators to tailor communication strategies. The Google Trends Supercharged-Glimpse tool offers enhanced features for analyzing information-seeking behavior and can be valuable for infodemiology research. Further research is needed to explore the evolving prevalence and impact of alpha-gal syndrome.

Global regionalization of heat environment quality perception based on K-means clustering and Google trends data

To effectively plan for the thermal environment in the face of climate change, it is crucial to consider regionalized approaches and people's perceptions of the phenomenon based on actual experiences. This study performs perception-based regionalization research of the thermal environment using Google Trends search query volume data. Global Google Trends data for 12 terms related to the thermal environment were collected from 2016 to 2022 and analyzed by time series and geographical units. The study found that the correlation between geographical unit data was higher than that of the time series units. To propose a global regionalization map, we used K-means clustering on the geographical Google Trends dataset and determined the optimal number of five clusters using the elbow method. Through a detailed analysis of each term for derived clusters A to E, the study revealed findings and implications that would contribute to the literature on the thermal environment. Finally, the perception-based global regionalization map was proposed. Overall, this novel approach to determining global regions based on people's perceptions of the thermal environment with Google Trends data provides insights for effective future thermal environment planning by analyzing the priority of characteristic groups and indicators by relevant regions for each cluster.

Web Search Query Volume Correlates With Prescription Volumes of Antidepressants and Antipsychotics in the Netherlands and United Kingdom: An Explorative Study.

The significant increase in Internet availability has resulted in a rise in search queries on health-related topics. Previous research has demonstrated the potential for analyzing web search query volume for nonpsychotropic prescription drugs, while studies on psychotropic drugs remain scarce. The aims of this study were to expand upon this scarce knowledge by investigating the relationship between web search query volumes and prescription volumes of antidepressants and antipsychotics in the United Kingdom and the Netherlands and to gain insight in topics of concern, such as withdrawal symptoms and discontinuation. Data were obtained for the United Kingdom and the Netherlands from January 2010 until January 2021. Prescription volume data for 5 antidepressants (paroxetine, fluoxetine, sertraline, citalopram, venlafaxine) and 5 antipsychotics (quetiapine, olanzapine, clozapine, aripiprazole, and risperidone) were obtained. Web search query volumes and data on related search queries of these substances were acquired from Google Trends. Descriptive statistics and Pearson correlation analyses were performed. A strong, positive, and statistically significant correlation between web search query volume and prescription volume was observed for most included substances in both the Netherlands and the United Kingdom. The search queries related to the included antidepressants and antipsychotics indicate important topics of concern for specific substances, such as withdrawal symptoms and discontinuation. Web search data from Google Trends could potentially be used as a proxy for prescribing trends of antidepressants and antipsychotics and to gain insight in topics of concern of users of these substances. These findings highlight the importance of providing reliable patient information, particularly regarding adverse effects, withdrawal, and discontinuation.

Performance Comparison of Predictive Methodologies for Carbon Emission Credit Price in the Korea Emission Trading System

Research related to the carbon-emission credit-price prediction model has only considered the effects of specific indicators, such as coal and oil prices, and only long-term prediction studies have been conducted. Recently, carbon emission credits have been recognized as investment assets, such as stocks and real estate. Accordingly, a carbon-emission credit prediction method is needed to establish an industrial strategy with low risk. In this study, an attempt was made to model the behavior of market participants in the time series model by analyzing the correlation between the search query volume data and the Korean Allowance Unit (KAU). Multiple Linear Regression Analysis (MRA) and Auto-Regressive Integrated Moving Average models were developed. In all price prediction models, the error of the prediction model at the 4th time was low. In the case of MRA, the error in the predicted near future price was small, but the error rate increased with increasing analysis period and prediction time. The error rate of ARIMA was lower than that of MRA, but it did not show a rapid change. These research findings will be beneficial to investigating and finding more rigid and reliable methodologies that can be used to predict various important values in similar fields in the future.

Sinus Surgery and Balloon Sinuplasty: What Do Patients Want to Know?

Limited knowledge exists regarding the information patients seek online about sinus procedures. The goals of our study were to identify the most searched online queries regarding functional endoscopic sinus surgery (FESS) and balloon sinuplasty and evaluate the sources to which patients are directed. Observational. Online Google Database. Google data were analyzed using the search engine optimization tool Ahrefs. People Also Ask (PAA) questions (extracted from Google searches) helped identify questions for analysis of search query volume. Search results were categorized into specific topics, and the source (eg, academic vs medical practice) of the information was identified. The JAMA benchmark criteria were used to determine the quality of the online resource. The most searched term (average monthly queries) on Google was "sinus surgery" (13,190), followed by "balloon sinuplasty" (9212). For FESS and balloon sinuplasty, most questions focused on treatment of sinusitis (71.64% vs 79.19%) and preoperative inquiries about sinus issues (11.50% vs 11.35%). Answers to PAA questions for FESS were obtained from academic sources at a higher frequency compared to balloon sinuplasty (26.7% vs 10.3%, P = .016) but a lower frequency from medical practice websites (15.2% vs 29.3%, P = .042). The mean (SD) JAMA scores for FESS and balloon sinuplasty sources were 1.59 (1.46) and 1.40 (1.46), respectively. There is a high volume of online search queries regarding FESS and balloon sinuplasty. The quality of the sources could be improved by addressing authorship, attribution, disclosure, and currency. This information may help otolaryngologists better address patient queries.

Common communicable diseases in the general population in France during the COVID-19 pandemic.

In France, social distancing measures have been adopted to contain the spread of COVID-19, culminating in national Lockdowns. The use of hand washing, hydro-alcoholic rubs and mask-wearing also increased over time. As these measures are likely to impact the transmission of many communicable diseases, we studied the changes in common infectious diseases incidence in France during the first year of COVID-19 circulation. We examined the weekly incidence of acute gastroenteritis, chickenpox, acute respiratory infections and bronchiolitis reported in general practitioner networks since January 2016. We obtained search engine query volume for French terms related to these diseases and sales data for relevant drugs over the same period. A periodic regression model was fit to disease incidence, drug sales and search query volume before the COVID-19 period and extrapolated afterwards. We compared the expected values with observations made in 2020. During the first lockdown period, incidence dropped by 67% for gastroenteritis, by 79% for bronchiolitis, by 49% for acute respiratory infection and 90% for chickenpox compared to the past years. Reductions with respect to the expected incidence reflected the strength of implemented measures. Incidence in children was impacted the most. Reduction in primary care consultations dropped during a short period at the beginning of the first lockdown period but remained more than 95% of the expected value afterwards. In primary care, the large decrease in reported gastroenteritis, chickenpox or bronchiolitis observed during the period where many barrier measures were implemented imply that the circulation of common viruses was reduced and informs on the overall effect of these measures.

Seasonal Variation and Global Public Interest in the Internet Searches for Osteoporosis.

Background To ascertain the seasonal pattern and global public interest in osteoporosis by evaluating search term popularity changes of the disease over a decade. Methods We applied Google Trends to retrieve search popularity scores for the term “osteoporosis” between January 01, 2004, and December 31, 2019. Cosinor analyses were conducted to examine the seasonality of osteoporosis, and analysis on osteoporosis-related topics including hot topics and rising-related topics was also performed. Results The cosinor analyses demonstrated a statistically significant seasonal variation in relative search volume of the “osteoporosis” in the world (p = 0.0083), USA (p < 0.001), UK (p < 0.001), Canada (p < 0.001), Ireland (p < 0.001), Australia (p < 0.001), and New Zealand (p < 0.001), with a peak in the late winter months and trough in the summer months. The peaks in late winter and valley in summer presented an approximately 6-month difference between hemispheres. The top 11 rising topics were denosumab, FRAX, hypocalcaemia, zoledronic acid, ibandronic acid, osteomyelitis, osteopenia, osteoarthritis, bone, calcium, and bone density. Conclusions Google search query volumes related to osteoporosis follow strong seasonal patterns with late winter peaks and summer troughs. Further studies aimed at elucidating the possible mechanisms behind seasonality in osteoporosis are needed. Moreover, Internet data including the top rising topics may alert physicians to strengthen the propaganda of osteoporosis timely, so as to further promote the development of public health interventions.

Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study.

BackgroundThe prevalence of chronic conditions such as obesity, hypertension, and diabetes is increasing in African countries. Many chronic diseases have been linked to risk factors such as poor diet and physical inactivity. Data for these behavioral risk factors are usually obtained from surveys, which can be delayed by years. Behavioral data from digital sources, including social media and search engines, could be used for timely monitoring of behavioral risk factors.ObjectiveThe objective of our study was to propose the use of digital data from internet sources for monitoring changes in behavioral risk factors in Africa.MethodsWe obtained the adjusted volume of search queries submitted to Google for 108 terms related to diet, exercise, and disease from 2010 to 2016. We also obtained the obesity and overweight prevalence for 52 African countries from the World Health Organization (WHO) for the same period. Machine learning algorithms (ie, random forest, support vector machine, Bayes generalized linear model, gradient boosting, and an ensemble of the individual methods) were used to identify search terms and patterns that correlate with changes in obesity and overweight prevalence across Africa. Out-of-sample predictions were used to assess and validate the model performance.ResultsThe study included 52 African countries. In 2016, the WHO reported an overweight prevalence ranging from 20.9% (95% credible interval [CI] 17.1%-25.0%) to 66.8% (95% CI 62.4%-71.0%) and an obesity prevalence ranging from 4.5% (95% CI 2.9%-6.5%) to 32.5% (95% CI 27.2%-38.1%) in Africa. The highest obesity and overweight prevalence were noted in the northern and southern regions. Google searches for diet-, exercise-, and obesity-related terms explained 97.3% (root-mean-square error [RMSE] 1.15) of the variation in obesity prevalence across all 52 countries. Similarly, the search data explained 96.6% (RMSE 2.26) of the variation in the overweight prevalence. The search terms yoga, exercise, and gym were most correlated with changes in obesity and overweight prevalence in countries with the highest prevalence.ConclusionsInformation-seeking patterns for diet- and exercise-related terms could indicate changes in attitudes toward and engagement in risk factors or healthy behaviors. These trends could capture population changes in risk factor prevalence, inform digital and physical interventions, and supplement official data from surveys.

The effect of online search volume on financial performance: Marketing insight from Google trends data of the top five US technology firms

ABSTRACT In this research, we examine the pivotal effects of search volume on technology firms’ financial performance and add incremental knowledge on the importance of online search data. We use panel data with ordinary least squares (OLS) regression analysis covering a fixed period to provide insights into the impact of the volume of online search queries of the top five U.S. technology firms’ brand names and products on their financial performance. Through a longitudinal study of the five most prominent technology firms in the United States, this research provides evidence that online search volume of a firm’s brand names is negatively related to firms’ performance (return on equity (ROE), return on assets (ROA), and Tobin’s Q), while search volume for a firm’s major products’ names is positively associated with ROE and ROA. Informed by accessibility-diagnosticity theory and construal level theory, we believe our findings hold because the negativity effect of online information is more saliently activated by higher-level construals (e.g., corporate brand information) than lower-level construals (e.g., product information). The findings provide new insights on search engine data interpretation. The theoretical and managerial contributions of these results are also discussed.

Google search volumes and the financial markets during the COVID-19 outbreak

During the outbreak of the COVID-19, concerns related to the severity of the pandemic have played a prominent role in investment decisions. In this paper, we analyze the relationship between public attention and the financial markets using search engine data from Google Trends. Our findings show that search query volumes in Italy, Germany, France, Great Britain, Spain, and the United States are connected with stock markets. The Italian Google Trends index is found to be the main driver of all the considered markets. Furthermore, the country-specific market impacts of COVID-19-related concerns closely follow the Italian lockdown process.

Internet search patterns reveal firearm sales, policies, and deaths

Firearm-related violence is a leading cause of morbidity and mortality and is at the center of a major public health and policy debate in the United States. Despite the critical role of data in informing this debate, accurate and comprehensive data on firearm sales and ownership is not readily available. In this study, we evaluate the potential of using firearm-related internet search queries as a complementary, freely available, and near-real-time data source for tracking firearm sales and ownership that enables analysis at finer geographic and temporal scales. (Here, we examine data by state and by month to compare with other data sources, but search engine volume can be analyzed by city and by the week or by day). We validate search query volume against available data on background checks in all 50 US states, and find that they are highly correlated over time (Pearson’s r = 0.96, Spearman’s ρ = 0.94) and space (Pearson’s r = 0.78, Spearman’s ρ = 0.76). We find that stratifying this analysis by gun type (long-gun vs. handgun) increases this correlation dramatically, across both time and space. We also find a positive association between firearm-related search query volume and firearm-related mortality (Pearson’s r = 0.87, Spearman’s ρ = 0.90), and a negative association with the strength of state-level firearm control policies (Pearson’s r = −0.82, Spearman’s ρ = −0.83). Based on these findings, we propose a framework for prospective surveillance that incorporates firearm-related internet search volume as a useful complementary data source to inform the public health policy debate on this issue.

Real-time estimation of disease activity in emerging outbreaks using internet search information.

Understanding the behavior of emerging disease outbreaks in, or ahead of, real-time could help healthcare officials better design interventions to mitigate impacts on affected populations. Most healthcare-based disease surveillance systems, however, have significant inherent reporting delays due to data collection, aggregation, and distribution processes. Recent work has shown that machine learning methods leveraging a combination of traditionally collected epidemiological information and novel Internet-based data sources, such as disease-related Internet search activity, can produce meaningful "nowcasts" of disease incidence ahead of healthcare-based estimates, with most successful case studies focusing on endemic and seasonal diseases such as influenza and dengue. Here, we apply similar computational methods to emerging outbreaks in geographic regions where no historical presence of the disease of interest has been observed. By combining limited available historical epidemiological data available with disease-related Internet search activity, we retrospectively estimate disease activity in five recent outbreaks weeks ahead of traditional surveillance methods. We find that the proposed computational methods frequently provide useful real-time incidence estimates that can help fill temporal data gaps resulting from surveillance reporting delays. However, the proposed methods are limited by issues of sample bias and skew in search query volumes, perhaps as a result of media coverage.

The Association Between PM2.5 and Depression in China.

While China has been experiencing unprecedented economic growth, depression is becoming one of the most striking social and mental health problems in recent years. Such a paradox to progress may partially be due to the notoriously poor air quality of the country. To verify this argument, we constructed an index of the prevalence of depression (IPD) using internet search query volumes in Baidu to proxy the potential depression and examined how IPD is associated with PM2.5, the major air pollutant in China. Our results from 2-way fixed effects models reveal that a 100 μg·m−3 increase in previous week’s PM2.5 in a city is significantly associated with 0.279 increase in its IPD, comparable to 7.34 hours decrease in weekly daylight, and such relationship is particularly pronounced in the spring and summer and in East and South areas. Our findings of large-scale pattern suggest that PM2.5 at current levels in China poses serious mental health risks.

Purchase Intentions on Social Media as Predictors of Consumer Spending

The paper addresses the problem of forecasting consumer expenditure from social media data. Previous research of the topic exploited the intuition that search engine traffic reflects purchase intentions and constructed predictive models of consumer behaviour from search query volumes. In contrast, we derive predictors from explicit expressions of purchase intentions found in social media posts. Two types of predictors created from these expressions are explored: those based on word embeddings and those based on topical word clusters. We introduce a new clustering method, which takes into account temporal co-occurrence of words, in addition to their semantic similarity, in order to create predictors relevant to the forecasting problem. The predictors are evaluated against baselines that use only macroeconomic variables, and against models trained on search traffic data. Conducting experiments with three different regression methods on Facebook and Twitter data, we find that both word embeddings and word clusters help to reduce forecasting errors in comparison to purely macroeconomic models. In most experimental settings, the error reduction is statistically significant, and is comparable to error reduction achieved with search traffic variables.

The association between PM2.5 exposure and suicidal ideation: a prefectural panel study

BackgroundSuicidal ideation is subject to serious underestimation among existing public health studies. While numerous factors have been recognized in affecting suicidal thoughts and behaviors (STB), the associated environmental risks have been poorly understood. Foremost among the various environment risks were air pollution, in particular, the PM2.5. The present study attempted to examine the relationship between PM2.5 level and local weekly index of suicidal ideation (ISI).MethodsUsing Internet search query volumes in Baidu (2017), the largest internet search engine in China, we constructed a prefectural panel data (278 prefectures, 52 weeks) and employed dynamic panel GMM system estimation to analyze the relationship between weekly concentration of PM2.5 (Mean = 87 μg·m− 3) and the index of suicidal ideation (Mean = 49.9).ResultsThe results indicate that in the spring and winter, a 10 μg·m− 3 increase in the prior week’s PM2.5 in a Chinese city is significantly associated with 0.020 increase in ISI in spring and a 0.007 increase in ISI in winter, after taking account other co-pollutants and meteorological conditions.ConclusionWe innovatively proposed the measure of suicidal ideation and provided suggestive evidence of a positive association between suicidal ideation and PM2.5 level.

SUN-113 Evaluating Awareness of Kidney Disease Using Google Trends

Kidney disease is a significant cause of mortality and morbidity worldwide. Important aspect of reducing kidney health burden entails awareness about kidney disease, for early detection and management. Various organizations around the world have developed campaigns to increase awareness. The aim of our study was to find out public awareness about kidney disease using Google Trends. Using Google Trends, a public web facility of Google Inc., we evaluated relative frequency of search of terms ‘kidney disease’, ‘chronic kidney disease ‘and ‘kidney failure’ in various combination to obtain highest yield search volume between 1st January 2004 and 1st October 2019 (n=190 months). The program assigns a reference value of 100 for the point of maximum popularity among the search terms, and provides relative monthly scores for all terms, which we termed relative interest scores (RIS). Therefore, the RIS is a surrogate for the popularity of the term. The mean RIS was then compared between each term. Trend analyses were performed. We compared linear models to show trends in the volume of searches for the United States and Worldwide. Model fit was good, and regression analysis showed significant trends over time for all searches. Use of search terms increased significantly over time (all models P < .05, Fig 1 A, B,C). All trends showed mostly upward trajectory for the entirety of the study time period. Peak interest was also evaluated based on time and place. The term “kidney disease” seems to be gaining popularity in the month of March, which coincides with kidney disease awareness month. There has been a marked and statistically significant rise in search query volume related to kidney disease since 2004, along with prevalence. It's encouraging to see trends of peaks in the month of March may be due to worldwide celebration of kidney disease month as well as kidney day. However, reason for peaks in other specific months should be explored and can be applied to future awareness campaigns for better effectiveness. Similarly, lessons from other successful campaigns for example Breast Cancer Awareness (Fig 1D) can be utilized to further increase effectiveness of kidney disease awareness campaigns. Use of Google trends allows as an alternative to surveillance of campaigns and awareness about health related topics.

High spatial and temporal detail in timely prediction of tourism demand

AbstractWhat happens in forecasting problems when high frequency and high spatial detail data encounter significant publication delays? In this paper, we consider a monthly dynamic panel data model, augmented by Google Trends search query volume data, for tourism demand forecasting at high spatial detail, in which one of the main aspects is represented by a publication delay ranging from 8 to 15 months. Some findings in the tourism literature already specify forecasting/nowcasting applications considering a realistic time delay but not for more than 3 months.

Correlations in Trends of Sinusitis-Related Online Google Search Queries in the United States

Online search query trends have been shown to correlate with real-life epidemiologic phenomena. The aim of this study was to analyze correlations in trends in Google online search volumes of sinusitis-related terms, including symptomatology and similar disease states. Terms clinically associated with "sinusitis" were determined by consensus. Terms of symptomatology were derived from the validated 22-item sinonasal outcome test (SNOT-22) as well as terminology encountered with the authors' clinical experience. Terms of disease states that could overlap in symptomatology with sinusitis were then chosen. Google Trends, an online tool for extracting relative frequencies from a public database of search queries, was used to query normalized monthly volumes in the United States from January 2004 to September 2017 of searches related to the topics decided upon by consensus. Bivariate Pearson correlation was used to compare the search queries. Online search volumes of "sinusitis" have a distinct seasonal variation, with consistent annual peaks and troughs. In terms of symptomatology, "postnasal drip," "nasal congestion," "cough," "rhinorrhea," and "sore throat" most highly correlated with "sinusitis" search volumes with statistical significance. "sinusitis" search query volume had a higher positive correlation with "common cold" and "acute sinusitis" than "chronic sinusitis" with regard to disease states. Trends in Google online search volumes over time of "sinusitis" symptomatology mimic real-world clinical phenomena and provide insight into the issues affecting the general population.

Infodemiological patterns in searching medication errors: relationship with risk management and shift work.

Western world health care systems have been trying to improve their efficiency and effectiveness in order to respond properly to population aging and non-communicable diseases epidemic. Treatment of the elderly population is becoming complex due to the high number of prescribed drugs because of multimorbidity. Errors in drugs administration in different health care related settings are an actual important issue due to different causes. Aim of this observational study is to measure the online interest in seeking medication errors information related to risk management and shift work. We investigated Google Trends® for popular search relating to medication errors, risk management and shift work. Relative search volumes (RSVs) were evaluated from 2008 to 2018. A comparison between RSV curves related to medication errors, risk management and shift work was carried out. Then, we compared the world to Italian search. RSVs were persistently higher for risk management than for medication errors (mean RSVs 069 vs. 48%) and RSVs were stably higher for medication errors than shift work (mean RSVs 48 vs. 22%). In Italy, RSVs were much lower compared to the rest of the world, and RSVs for medication errors during the study period were negligible. Mean RSVs for risk management and shift work were 3 and 25%, respectively. RSVs related to medication errors and clinical risk management were correlated (r=0.520, p<0.0001). Google Trends® search query volumes related to medication errors, risk management and shift work are different. RSVs for risk management are higher, and they are correlated with medication errors. Also, shift work search appears to be lower. These results should be interpreted in order to correctly evaluate how to decrease the number of medication errors in different health care related setting.

Increasing Public Interest in Stem Cell Injections for Osteoarthritis of the Hip and Knee: A Google Trends Analysis

BackgroundStem cell injections are being offered to patients as a nonoperative treatment for osteoarthritis of the hip and knee. To our knowledge, no peer-reviewed data exist to document the usage frequency of these injections nor to quantify the public interest in these injections. We sought to use Google Trends to provide a quantitative analysis of interest in hip and knee stem cell injections at the population level. MethodsGoogle Trends search parameters were set to obtain query data from January 2010 through December 2017. ‘Arthritis,’ ‘osteoarthritis,’ ‘stem cell,’ ‘injection,’ ‘knee,’ and ‘hip’ were entered in various combinations to obtain the highest yield search volume. Trend analyses were performed. ResultsSix linear models were generated to show trends in the volume of searches for the United States and the World. Model fit was good, and regression analysis showed significant trends over time for all searches. Use of search terms increased significantly over time (all models P < .001). Adjusted R-square values ranged from 54.4% to 78.1%. All trends showed an upward trajectory for the entirety of the study time period. ConclusionThere has been a marked and statistically significant rise in search query volume related to stem cells and osteoarthritis of the hip and knee since 2010. Online interest in stem cell injections may suggest increased utilization of these procedures. Well-designed clinical studies are required to keep pace with the rising popularity and public interest in this intervention for hip and knee arthritis.