Real-time sharing and expression of migraine headache suffering on Twitter: a cross-sectional infodemiology study

Classifying migraine using PET compressive big data analytics of brain's μ-opioid and D2/D3 dopamine neurotransmission

Introduction: Migraine is a common and debilitating pain disorder associated with dysfunction of the central nervous system. Advanced magnetic resonance imaging (MRI) studies have reported relevant pathophysiologic states in migraine. However, its molecular mechanistic processes are still poorly understood in vivo. This study examined migraine patients with a novel machine learning (ML) method based on their central μ-opioid and dopamine D2/D3 profiles, the most critical neurotransmitters in the brain for pain perception and its cognitive-motivational interface. Methods: We employed compressive Big Data Analytics (CBDA) to identify migraineurs and healthy controls (HC) in a large positron emission tomography (PET) dataset. 198 PET volumes were obtained from 38 migraineurs and 23 HC during rest and thermal pain challenge. 61 subjects were scanned with the selective μ-opioid receptor (μOR) radiotracer [11C]Carfentanil, and 22 with the selective dopamine D2/D3 receptor (DOR) radiotracer [11C]Raclopride. PET scans were recast into a 1D array of 510,340 voxels with spatial and intensity filtering of non-displaceable binding potential (BPND), representing the receptor availability level. We then performed data reduction and CBDA to power rank the predictive brain voxels. Results: CBDA classified migraineurs from HC with accuracy, sensitivity, and specificity above 90% for whole-brain and region-of-interest (ROI) analyses. The most predictive ROIs for μOR were the insula (anterior), thalamus (pulvinar, medial-dorsal, and ventral lateral/posterior nuclei), and the putamen. The latter, putamen (anterior), was also the most predictive for migraine regarding DOR D2/D3 BPND levels. Discussion: CBDA of endogenous μ-opioid and D2/D3 dopamine dysfunctions in the brain can accurately identify a migraine patient based on their receptor availability across key sensory, motor, and motivational processing regions. Our ML-based findings in the migraineur's brain neurotransmission partly explain the severe impact of migraine suffering and associated neuropsychiatric comorbidities.

Surveillance of communicable diseases using social media: A systematic review

BACKGROUND

Communicable diseases pose a severe threat to public health and economic growth. The traditional methods that are used for public health surveillance, however, involve many drawbacks, such as being labor intensive to operate and resulting in a lag between data collection and reporting. To effectively address the limitations of these traditional methods and to mitigate the adverse effects of these diseases, a proactive and real-time public health surveillance system is needed. Previous studies have indicated the usefulness of performing text mining on social media.

OBJECTIVE

To conduct a systematic review of the literature that used textual content published to social media for the purpose of the surveillance and prediction of communicable diseases.

METHODOLOGY

Broad search queries were formulated and performed in four databases. Both journal articles and conference materials were included. The quality of the studies, operationalized as reliability and validity, was assessed. This qualitative systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

RESULTS

Twenty-three publications were included in this systematic review. All studies reported positive results for using textual social media content to surveille communicable diseases. Most studies used Twitter as a source for these data. Influenza was studied most frequently, while other communicable diseases received far less attention. Journal articles had a higher quality (reliability and validity) than conference papers. However, studies often failed to provide important information about procedures and implementation.

CONCLUSION

Text mining of health-related content published on social media can serve as a novel and powerful tool for the automated, real-time, and remote monitoring of public health and for the surveillance and prediction of communicable diseases in particular. This tool can address limitations related to traditional surveillance methods, and it has the potential to supplement traditional methods for public health surveillance.

Infodemiology of cluster headache seasonality: A proof of concept by a Google Trends analysis

BACKGROUND

Cluster headache is commonly reported to follow an annual pattern with a peak in the spring and a second peak in autumn. Patients with headache frequently use search engines, such as Google, to look for terms related to their disease, creating trend data that can be analyzed with Google Trends. Indeed, Google Trends has been used for surveillance studies and can provide indirect estimates of the burden of diseases and symptoms. The present cross-sectional study investigated the seasonality of searches for "cluster headache" in the northern and southern hemispheres using 10 years of Google Trends data.

METHODS

The term "cluster headache" or its translation in the 10 most spoken languages in the world was searched on Google Trends to obtain relative search volumes (from 0 to 100), in order to compare variations in searches across periods. Twenty-eight countries were selected according to the following criteria: (1) a relative search volume of >40 for the term for cluster headache; and (2) a population of at least 5 million inhabitants. For statistical purposes, countries were grouped in relation to hemisphere (northern or southern). Relative search volumes were extracted from January 2012 to January 2022 and analyzed according to two subgroups based on meteorological seasons (summer and winter vs. spring and autumn).

RESULTS

A seasonal trend for in searches for cluster headache was found worldwide exhibiting higher relative search volumes in spring and autumn compared with summer and winter (17 [0, 39] vs. 13 [0, 37]; p = 0.016).

CONCLUSION

Higher search volumes for the term during the meteorological seasons of spring and autumn clearly reflect a circannual pattern of cluster headache occurrence, representing new evidence for its seasonality.

Prevalence and risk factors of migraine headache among university students: A cross-sectional study in Lebanon

Background Migraine constitutes a major public health concern since it negatively affects both the quality of life and the productivity of patients. Migraine among students can cause impaired academic performance and limit their daily activities. Objective This study aims to assess the prevalence of migraine among university students using the ID Migraine screening tool and to evaluate risk factors associated with migraine. Methods A cross-sectional study was performed over a period of six months targeting 1144 university students recruited from the different faculties. Results Migraine was suggested in 35.8% of subjects based on ID-Migraine. Migraine prevalence was significantly higher among women (42% versus 23.3% of men). After adjusting for covariates, women, daily coffee consumption, having unorganized meals, eating fast food and fasting were the main predictors of migraine.  Almost 41% of migraine students had a family history of migraine and only 26.7% sought medical help. Interestingly, most of the migraine students (84.8%) took headache medications without referring them to their doctor. Conclusion Recognizing headache risk factors among migraine students and adopting lifestyle changes accordingly can be an effective strategy to prevent the chronification of the attacks, decrease headache frequency and improve patients’ quality of life.

Social Media Listening and Digital Profiling Study of People with Headache and Migraine: A Retrospective Infodemiology Study (Preprint)

BACKGROUND

There is an unmet need for a better understanding and management of headache, particularly migraine, beyond specialist centers, which may be facilitated using digital technology.

OBJECTIVE

The objective of this study was to identify where, when, and how people with headache and migraine describe their symptoms and the nonpharmaceutical and medicinal treatments used as indicated on social media.

METHODS

Social media sources, including Twitter, web-based forums, blogs, YouTube, and review sites, were searched using a predefined search string related to headache and migraine. The real-time data from social media posts were collected retrospectively for a 1-year period from January 1, 2018, to December 31, 2018 (Japan), or a 2-year period from January 1, 2017, to December 31, 2018 (Germany and France). The data were analyzed after collection, using content analysis and audience profiling.

RESULTS

A total of 3,509,828 social media posts related to headache and migraine were obtained from Japan in 1 year and 146,257 and 306,787 posts from Germany and France, respectively, in 2 years. Among social media sites, Twitter was the most used platform across these countries. Japanese sufferers used specific terminology, such as "tension headaches" or "cluster headaches" (36%), whereas French sufferers even mentioned specific migraine types, such as ocular (7%) and aura (2%). The most detailed posts on headache or migraine were from Germany. The French sufferers explicitly mentioned "headache or migraine attacks" in the "evening (41%) or morning (38%)," whereas Japanese mentioned "morning (48%) or night (27%)" and German sufferers mentioned "evening (22%) or night (41%)." The use of "generic terms" such as medicine, tablet, and pill were prevalent. The most discussed drugs were ibuprofen and naproxen combination (43%) in Japan; ibuprofen (29%) in Germany; and acetylsalicylic acid, paracetamol, and caffeine combination (75%) in France. The top 3 nonpharmaceutical treatments are hydration, caffeinated beverages, and relaxation methods. Of the sufferers, 44% were between 18 and 24 years of age.

CONCLUSIONS

In this digital era, social media listening studies present an opportunity to provide unguided, self-reported, sufferers' perceptions in the real world. The generation of social media evidence requires appropriate methodology to translate data into scientific information and relevant medical insights. This social media listening study showed country-specific differences in headache and migraine symptoms experienced and in the times of the day and treatments used. Furthermore, this study highlighted the prevalence of social media usage by younger sufferers compared to that by older sufferers.

Relationship between internet research data of oral neoplasms and public health programs in the European Union

BACKGROUND

Tobacco and alcohol are the main risk factors for oral squamous cell carcinoma, the low survival rate of which is a public health problem. European-wide health policies (a prevention campaign, tobacco packaging) have been put in place to inform the population of the risks associated with consumption. Due to the increase in smoking among women, the incidence of this disease remains high. The identification of internet research data on the population could help to measure the impact of and better position these preventive measures. The objective was to analyze a potential temporal association between public health programs and interest in oral cancers on the internet in the European Union (EU).

METHODS

A search of data from Google ©, Wikipedia © and Twitter © users in 28 European countries relating to oral cancer between 2004 and 2019 was completed. Bibliometric analysis of press and scientific articles over the same period was also performed. The association between these data and the introduction of public health programs in Europe was studied.

RESULTS

There was a temporal association between changes in tobacco packaging and a significant increase in internet searches for oral cancer in seven countries. Unlike national policies and ad campaigns, the European awareness program Make Sense has had no influence on internet research. There was an asymmetric correlation in internet searches between publications on oral cancer from scientific articles or "traditional" media (weak association) and those from internet media such as Twitter © or Wikipedia © (strong association).

CONCLUSION

Our work highlights seven areas around which oral cancer awareness in Europe could be refocused, such as a change in the communication of health warnings on cigarette packs, the establishment of a more explicit campaign name regarding oral cancer, the involvement of public figures and associations in initiatives to be organized at the local level and the strengthening of awareness of the dangers of tobacco in the development of oral cancer.

Headache in transgender and gender-diverse patients: A narrative review

OBJECTIVE

To summarize the unique aspects of managing headache in gender minorities and current research in this area including the potential relationship between gender-affirming hormone therapy (GAHT) and headache.

BACKGROUND

The study of headache in gender minorities is intrinsically important. Gender minorities are medically underserved, and their medical care to date has been limited by socioeconomic disadvantages including stigma and an unsupportive clinical environment. Despite the rising population of transgender and gender-diverse adults and youth, headache research has also been limited. Knowledge of hormonal effects on headache in cisgender patients raises the question of possible effects of GAHT on transgender patients.

METHODS/RESULTS

The manuscript is a narrative review of current best practices in treating transgender patients, including the use of appropriate terminology and ways to create a supportive environment. It also contains current guidelines on GAHT and reviews drug-drug interactions and secondary headache related to hormone therapy. We also review transgender headache research and related research on hormonal effects on headache in cisgender individuals.

CONCLUSION

Creating a supportive environment for transgender and gender-diverse patients and being knowledgeable about GAHT are key to providing quality headache care. This review identifies further research needs for this population including the epidemiology of headache disorders in sexual minorities and the potential effects of GAHT on headache disorders in transgender patients.

How advocacy affects Twitter migraine conversations: A pilot cross-sectional survey of Northeast American “migraine” landscape on Twitter from May to June 2020

Background:

Twitter is a leading microblogging platform, with over 126 million daily active users as of 2019, which allows for large-scale analysis of tweets related to migraine. June 2020 encompassed the National Migraine and Headache Awareness Month in the United States and the American Headache Society’s virtual annual conference, which offer opportunities for us to study online migraine advocacy.

Objective:

We aim to study the content of individual tweets about migraine, as well as study patterns of other topics that were discussed in those tweets. In addition, we aim to study the sources of information that people reference within their tweets. Thirdly, we want to study how online awareness and advocacy movements shape these conversations about migraine.

Methods:

We designed a Twitter robot that records all unique public tweets containing the word “migraine” from May 8th, 2020 to June 23rd, 2020, within a 400 km radius of New Brunswick, New Jersey, United States. We built two network analysis models, one for the months of May 2020 and June 2020. The model for the month of May served as a control group for the model for the month of June, the Migraine Awareness Month. Our network model was developed with the following rule: if two hashtag topics co-exist in a single tweet, they are considered nodes connected by an edge in our network model. We then determine the top 30 most important hashtags in the month of May and June through applications of degree, between-ness, and closeness centrality. We also generated highly connected subgraphs (HCS) to categorize clusters of conversations within each of our models. Finally, we tally the websites referenced by these tweets during each month and categorized these websites according to the HCS subgroups.

Results:

Migraine advocacy related tweets are more popular in June when compared to May as judged by degree and closeness centrality measurements. They remained unchanged when judged by between-ness centralities. The HCS algorithm categorizes the hashtags into a large single dominant conversation in both months. In each of the months, advocacy related hashtags are apart of each of the dominant conversation. There are more hashtag topics as well as more unique websites referenced in the dominant conversation in June than in May. In addition, there are many smaller subgroups of migraine-related hashtags, and in each of these subgroups, there are a maximum of two websites referenced.

Conclusion:

We find a network analysis approach to be fruitful in the area of migraine social media research. Migraine advocacy tweets on Twitter not only rise in popularity during migraine awareness month but also may potentially bring in more diverse sources of online references into the Twitter migraine conversation. The smaller subgroups we identified suggest that there are marginalized conversations referencing a limited number of websites, creating a possibility of an “echo chamber” phenomenon. These subgroups provide an opportunity for targeted migraine advocacy. Our study therefore highlights the success as well as potential opportunities for social media advocacy on Twitter.

Measures of the Degree of Departure from Ignorable Sample Selection

With the current focus of survey researchers on "big data" that are not selected by probability sampling, measures of the degree of potential sampling bias arising from this nonrandom selection are sorely needed. Existing indices of this degree of departure from probability sampling, like the R-indicator, are based on functions of the propensity of inclusion in the sample, estimated by modeling the inclusion probability as a function of auxiliary variables. These methods are agnostic about the relationship between the inclusion probability and survey outcomes, which is a crucial feature of the problem. We propose a simple index of degree of departure from ignorable sample selection that corrects this deficiency, which we call the standardized measure of unadjusted bias (SMUB). The index is based on normal pattern-mixture models for nonresponse applied to this sample selection problem and is grounded in the model-based framework of nonignorable selection first proposed in the context of nonresponse by Don Rubin in 1976. The index depends on an inestimable parameter that measures the deviation from selection at random, which ranges between the values zero and one. We propose the use of a central value of this parameter, 0.5, for computing a point index, and computing the values of SMUB at zero and one to provide a range of the index in a sensitivity analysis. We also provide a fully Bayesian approach for computing credible intervals for the SMUB, reflecting uncertainty in the values of all of the input parameters. The proposed methods have been implemented in R and are illustrated using real data from the National Survey of Family Growth.

Efficacy of a WeChat based intervention to adherence to secondary prevention in patients undergoing coronary artery bypass graft in China: A randomized controlled trial

INTRODUCTION

We assessed whether the social media-based (WeChat) intervention integrated with follow-up care could improve adherence to drugs, lifestyle changes and clinical risk markers in patients undergoing coronary artery bypass graft (CABG) in China.

METHODS

We randomized patients at hospital discharge following CABG to intervention group or control care in China. The intervention is a structured programme of cardiac health education, medication reminders and cardiologist-based follow-up service using WeChat platform. The control group maintains a routine practice pattern. The primary outcome is adherence to cardioprotective medications measured for 12 months after discharge. We also evaluated the lifestyle modifications and clinical risk markers at 12 months.

RESULTS

A total of 164 participants completed the trial for analysis. The intervention group had significantly greater adherence to statins use 98.6% vs. 75.0% (p < 0.01), beta-blockers 93.4% vs. 69.3% (p < 0.01) and aspirin 98.8% vs. 87.8% (p < 0.001). The intervention group had significantly greater adherence to regular physical activity (64.2% vs. 48.2%; p < 0.039). Furthermore, intervention versus standard group at 12 months had significantly lower mean systolic blood pressure and low-density lipoprotein cholesterol (p < 0.05).

DISCUSSION

A WeChat-based intervention strategy in post-CABG patients improved adherence to medications, including statin, aspirin and beta-blockers, and regular physical activity and resulted in an improvement in systolic blood pressure and low-density lipoprotein cholesterol level.

'Dr. Google, I have a stomach ache' - seasonal variations in abdominal pain: a 4-year retrospective data analysis from Google Ads keywords planner

BACKGROUND

Internet traffic may reflect the prevalence of real-world ailments. The aim of this study was to analyse queries associated with abdominal pain and to investigate seasonal frequency and trends of searches in Poland.

METHODS

We analysed mean search volume from March 2015 to February 2019 of keywords associated with 'abdominal pain' in the Polish language generated from the Google Ads Planner. Keywords were categorised by location, feature or type of pain, concurrent symptoms, frequency and patient groups, etc. We compared differences in search volumes across seasons and years.

RESULTS

Overall, 24,673,430 queries were associated with abdominal pain in the period analysed. The number of searches associated with abdominal pain in winter increased by ~30% compared with queries searched in summer. There were significantly more searches related to abdominal pain located in the epigastric, umbilical, hypogastrium, left lumbar, left iliac and right iliac regions combined with vomiting or fever, and acute or burning sensation in cold months. We did not observe any specific patterns of pain in the right lumbar or left hypochondrium region, co-presence of flatulence, acute, cramping or persistent/chronic or strong abdominal pain.

CONCLUSION

Internet queries associated with abdominal pain and related factors have increased in the past 4 years and present a seasonal pattern. Google Ads may be a valuable tool to assess the prevalence of complaints in under-researched regions. The observed trends in queries related to abdominal pain merely reflect the scale of the problem. The reported pattern should be verified in epidemiological studies.

"Dr. Google, I am in Pain"-Global Internet Searches Associated with Pain: A Retrospective Analysis of Google Trends Data

We aimed to rank the most common locations of pain among Google users globally and locally and analyze secular and seasonal trends in pain-related searches in the years 2004-2019. We used data generated by Google Trends (GT) to identify and analyze global interest in topics (n = 24) related to locations of pain and how these progressed over time. We analyzed secular trends and time series decomposition to identify seasonal variations. We also calculated the interest in all topics with reference to the relative search volume (RSV) of "Abdominal pain". Google users were most commonly interested in "Headache" (1.30 [times more frequently than "Abdominal pain"]), "Abdominal pain" (1.00), and "Back pain" (0.84). "Headache" was the most frequent search term in n = 41 countries, while "Abdominal pain" was the most frequent term in n = 27 countries. The interest in all pain-related topics except "Dyspareunia" increased over time. The sharpest increase was observed for "Abdominal pain" (5.67 RSV/year), and "Toothache" (5.52 RSV/year). Most of the topics revealed seasonal variations. Among pain-related topics, "Headache," "Abdominal pain," and "Back pain" interested most Google users. GT is a novel tool that allows retrospective investigation of complaints among Internet users.

Headache Hygiene in Pediatrics: Brushing Up on the Basics

Headache hygiene refers to self-management behaviors and practices aimed at reducing headache-related disability and improving self-efficacy. Although self-management interventions have an established place in the management of a wide range of chronic conditions, there is still not a standardized approach to this in pediatric headache. In this article, we focus on headache hygiene approaches including education, lifestyle interventions, and psychologic interventions. We also present our center's resource compilation, made available to patients by quick response code technology, as an example of a structured approach to headache hygiene. Further work should explore a standardized approach to headache hygiene and strategies to support adherence, including the use of technology as an innovative health care delivery pathway.

Sentiment analysis of real-world migraine tweets for population research

Background:

Migraine is a highly prevalent disorder that is typically episodic in nature. Social network data reflecting personal commentary on everyday life patterns, including those interrupted by migraine, represent a unique window into the real-life experience of those willing to share them. The experience of a migraine attack might be captured by twitter text data, and this information might be used to complement our current knowledge of activity in the general population and even lead to enhanced prediction.

Objective:

To characterize tweets reporting migraine activity and to explore their social-behavior features as foundation for further investigations.

Methods:

A longitudinal cohort study utilizing 1 month of Twitter data from November to December 2014 was conducted. Tweets containing the word “migraine” were extracted, preprocessed, and managed using natural language processing (NLP) techniques. User behavior profiles including tweeting frequencies, high-frequency words, and sentimental presentations were reported and analyzed.

Results:

During the observation period, 98,622 tweets were captured from 77,335 different users. The overall sentiment of tweets was slightly negative for expressive tweets but neutral for informative tweets. Among posted negative expressive tweets, we found a strong tendency that high-frequent expressions were those with the extreme sentiment, and profanity was common.

Conclusions:

Twitter users with migraine showed distinct sentimental patterns while suffering from disease onsets exemplified by posting tweets with extreme negative sentiments.

Big Data, Computational Social Science, and Health Communication: A Review and Agenda for Advancing Theory

Contemporary research on health communication has been marked by the presence of big data and computational social science (CSS) techniques. The relative novelty of these approaches makes it worthwhile to consider their status and potential for advancing health communication scholarship. This essay offers an introduction focusing on how big data and CSS techniques are being employed to study health communication and their utility for theory development. Key trends in this body of research are summarized, including the use of big data and CSS for examining public perceptions of health conditions or events, investigating network-related dimensions of health phenomena, and illness monitoring. The implications of big data and CSS for health communication theory are also evaluated. Opportunities presented by big data and CSS to help extend existing theories and build new communication theories are discussed.

Indices of non-ignorable selection bias for proportions estimated from non-probability samples

Rising costs of survey data collection and declining response rates have caused researchers to turn to non-probability samples to make descriptive statements about populations. However, unlike probability samples, non-probability samples may produce severely biased descriptive estimates due to selection bias. The paper develops and evaluates a simple model-based index of the potential selection bias in estimates of population proportions due to non-ignorable selection mechanisms. The index depends on an inestimable parameter ranging from 0 to 1 that captures the amount of deviation from selection at random and is thus well suited to a sensitivity analysis. We describe modified maximum likelihood and Bayesian estimation approaches and provide new and easy-to-use R functions for their implementation. We use simulation studies to evaluate the ability of the proposed index to reflect selection bias in non-probability samples and show how the index outperforms a previously proposed index that relies on an underlying normality assumption. We demonstrate the use of the index in practice with real data from the National Survey of Family Growth.

Hospitalized patients quantify verbal pain intensity descriptors: methodological issues and values for 26 descriptors

Patients often tell others about their pain using their own verbal descriptors of pain intensity, but the meaning of this pain language is not universally evident, which could contribute to misinterpretation about pain severity. The study purpose was to discover the intensity values of verbal pain intensity descriptors. The 248 randomly selected inpatients used a visual analogue scale to assign a value to each of 26 pain intensity descriptors. Each participant completed 36 randomly ordered visual analogue scales, 10 of which were replications. Except for descriptors with medians close to 0 or 100 mm, there was large, across-person variability for the descriptors. For example, medians ± SD for some exemplar descriptors were no pain 0.7 ± 2.4; mild 16.2 ± 12.2; discomforting 31.3 ± 22.2; distressing 55.3 ± 24; horrible 87.8 ± 13.6; and excruciating 94.6 ± 9.3. Test-retest reliability indicated small within-person variability on scores assigned to each descriptor. Thirteen descriptors showed some statistically significant but rather small effects of presentation order. Findings contribute estimates for the magnitude of pain represented by each of the 26 descriptors. Clinicians, text data miners, and researchers should consider these values as they interpret the meaning of the descriptors that they hear in daily practice or research settings or that they find in electronic health records, email messages, or social media posts. Despite the wide variability in the magnitude of each descriptor, findings provide insights about the intensity of pain when individuals use verbal pain intensity descriptors in conversations, social media, or clinical encounters.

Estimating the Health-Related Quality of Life of Twitter Users Using Semantic Processing

Social media presents a rich opportunity to gather health information with limited intervention through the analysis of completely unstructured and unlabeled microposts. We sought to estimate the health-related quality of life (HRQOL) of Twitter users using automated semantic processing methods. We collected tweets from 878 Twitter users recruited through online solicitation and in-person contact with patients. All participants completed the four-item Centers for Disease Control Healthy Days Questionnaire at the time of enrollment and 30 days later to measure “ground truth” HRQOL. We used a combination of document frequency analysis, sentiment analysis, topic analysis, and concept mapping to extract features from tweets, which we then used to estimate dichotomized HRQOL (“high” vs. “low”) using logistic regression. Binary HRQOL status was estimated with moderate performance (AUC=0.64). This result indicates that free-range social media data only offers a window into HRQOL, but does not afford direct access to current health status.

Pediatric and Adolescent Headache

PURPOSE OF REVIEW

This article provides the practicing neurologist with a comprehensive, evidence-based approach to the diagnosis and management of headache in children and adolescents, with a focus on migraine.

RECENT FINDINGS

Four triptans are now labeled by the US Food and Drug Administration (FDA) for acute migraine treatment in adolescents, and rizatriptan is labeled for use in children age 6 and older. For preventive migraine treatment, the Childhood and Adolescent Migraine Prevention trial demonstrated that approximately 60% of children and adolescents with migraine will improve with a three-pronged treatment approach that includes: (1) lifestyle management counseling (on sleep, exercise, hydration, caffeine, and avoidance of meal skipping); (2) optimally dosed acute therapy, specifically nonsteroidal anti-inflammatory drugs and triptans; and (3) a preventive treatment that has some evidence for efficacy. For the remaining 40% of children and adolescents, and for those who would not have qualified for the Childhood and Adolescent Migraine Prevention trial because of having continuous headache or medication-overuse headache, the clinician's judgment remains the best guide to preventive therapy selection.

SUMMARY

Randomized placebo-controlled trials have been conducted to guide first-line acute and preventive migraine treatments in children and adolescents. Future research is needed to guide treatment for those with more refractory migraine, as well as for children and adolescents who have other primary headache disorders.

Feasibility of a Real-Time Clinical Augmented Reality and Artificial Intelligence Framework for Pain Detection and Localization From the Brain

Background

For many years, clinicians have been seeking for objective pain assessment solutions via neuroimaging techniques, focusing on the brain to detect human pain. Unfortunately, most of those techniques are not applicable in the clinical environment or lack accuracy.

Objective

This study aimed to test the feasibility of a mobile neuroimaging-based clinical augmented reality (AR) and artificial intelligence (AI) framework, CLARAi, for objective pain detection and also localization direct from the patient’s brain in real time.

Methods

Clinical dental pain was triggered in 21 patients by hypersensitive tooth stimulation with 20 consecutive descending cold stimulations (32°C-0°C). We used a portable optical neuroimaging technology, functional near-infrared spectroscopy, to gauge their cortical activity during evoked acute clinical pain. The data were decoded using a neural network (NN)–based AI algorithm to classify hemodynamic response data into pain and no-pain brain states in real time. We tested the performance of several networks (NN with 7 layers, 6 layers, 5 layers, 3 layers, recurrent NN, and long short-term memory network) upon reorganized data features on pain diction and localization in a simulated real-time environment. In addition, we also tested the feasibility of transmitting the neuroimaging data to an AR device, HoloLens, in the same simulated environment, allowing visualization of the ongoing cortical activity on a 3-dimensional brain template virtually plotted on the patients’ head during clinical consult.

Results

The artificial neutral network (3-layer NN) achieved an optimal classification accuracy at 80.37% (126,000/156,680) for pain and no pain discrimination, with positive likelihood ratio (PLR) at 2.35. We further explored a 3-class localization task of left/right side pain and no-pain states, and convolutional NN-6 (6-layer NN) achieved highest classification accuracy at 74.23% (1040/1401) with PLR at 2.02.

Conclusions

Additional studies are needed to optimize and validate our prototype CLARAi framework for other pains and neurologic disorders. However, we presented an innovative and feasible neuroimaging-based AR/AI concept that can potentially transform the human brain into an objective target to visualize and precisely measure and localize pain in real time where it is most needed: in the doctor’s office.

International Registered Report Identifier (IRRID)

RR1-10.2196/13594

Not Just a Headache: Qualitative Study About Web-Based Self-Presentation and Social Media Use by People With Migraine

BACKGROUND

To help with a long-term but invisible medical condition such as migraine, many people seek information and support on social media. The effect of using social media for people with migraine is not fully understood and remains to be investigated.

OBJECTIVE

The aim of this study was to describe how people with migraine use social media and how social media use affects their identity and sense of self.

METHODS

A total of 20 participants who experienced migraine were recruited via migraine-specific charities. Semistructured interviews were conducted with questions based on a topic guide. Interviews were transcribed verbatim, and transcripts were analyzed using thematic analysis.

RESULTS

People with migraine are using social media to obtain information to better understand their condition and treatment options. Social media offers instant access to continuous information and social support. This exchange of social support and information was viewed as mutually beneficial. Participants viewed social media as an outlet to vent frustrations and validate the migraine experience. Several participants pointed out that the invisible and episodic nature of migraine can lead to societal misunderstanding of the impact and or severity of their condition. Some participants masked their online migraine-related behavior using different sites or closed online groups to control who saw their migraine-related content. Participating in closed social media groups sometimes changed Web-based behavior in other areas of the platform. This illustrates the complex relationship between migraine, social media, and identity.

CONCLUSIONS

How migraine is part of an individual's identity and how this is represented online can vary. Social media can provide people who experience migraine with instant and continuous access to support and information, from a group of empathic others with similar lived experiences. Social media is used to validate the illness experience, as well as provide reassurance and help reduce feelings of isolation.

Perception of Older Adults Toward Smartwatch Technology for Assessing Pain and Related Patient-Reported Outcomes: Pilot Study

Background

Chronic pain, including arthritis, affects about 100 million adults in the United States. Complexity and diversity of the pain experience across time and people and its fluctuations across and within days show the need for valid pain reports that do not rely on patient’s long-term recall capability. Smartwatches can be used as digital ecological momentary assessment (EMA) tools for real-time collection of pain scores. Smartwatches are generally less expensive than smartphones, are highly portable, and have a simpler user interface, providing an excellent medium for continuous data collection and enabling a higher compliance rate.

Objective

The aim of this study was to explore the attitudes and perceptions of older adults towards design and technological aspects of a smartwatch framework for measuring patient report outcomes (PRO) as an EMA tool.

Methods

A focus group session was conducted to explore the perception of participants towards smartwatch technology and its utility for PRO assessment. Participants included older adults (age 65+), with unilateral or bilateral symptomatic knee osteoarthritis. A preliminary user interface with server communication capability was developed and deployed on 10 Samsung Gear S3 smartwatches and provided to the users during the focus group. Pain was designated as the main PRO, while fatigue, mood, and sleep quality were included as auxiliary PROs. Pre-planned topics included participants’ attitude towards the smartwatch technology, usability of the custom-designed app interface, and suitability of the smartwatch technology for PRO assessment. Discussions were transcribed, and content analysis with theme characterization was performed to identify and code the major themes.

Results

We recruited 19 participants (age 65+) who consented to take part in the focus group study. The overall attitude of the participants toward the smartwatch technology was positive. They showed interest in the direct phone-call capability, availability of extra apps such as the weather apps and sensors for tracking health and wellness such as accelerometer and heart rate sensor. Nearly three-quarters of participants showed willingness to participate in a one-year study to wear the watch daily. Concerns were raised regarding usability, including accessibility (larger icons), notification customization, and intuitive interface design (unambiguous icons and assessment scales). Participants expressed interest in using smartwatch technology for PRO assessment and the availability of methods for sharing data with health care providers.

Conclusions

All participants had overall positive views of the smartwatch technology for measuring PROs to facilitate patient-provider communications and to provide more targeted treatments and interventions in the future. Usability concerns were the major issues that will require special consideration in future smartwatch PRO user interface designs, especially accessibility issues, notification design, and use of intuitive assessment scales.

Characterizing Tweet Volume and Content About Common Health Conditions Across Pennsylvania: Retrospective Analysis

Background

Tweets can provide broad, real-time perspectives about health and medical diagnoses that can inform disease surveillance in geographic regions. Less is known, however, about how much individuals post about common health conditions or what they post about.

Objective

We sought to collect and analyze tweets from 1 state about high prevalence health conditions and characterize the tweet volume and content.

Methods

We collected 408,296,620 tweets originating in Pennsylvania from 2012-2015 and compared the prevalence of 14 common diseases to the frequency of disease mentions on Twitter. We identified and corrected bias induced due to variance in disease term specificity and used the machine learning approach of differential language analysis to determine the content (words and themes) most highly correlated with each disease.

Results

Common disease terms were included in 226,802 tweets (174,381 tweets after disease term correction). Posts about breast cancer (39,156/174,381 messages, 22.45%; 306,127/12,702,379 prevalence, 2.41%) and diabetes (40,217/174,381 messages, 23.06%; 2,189,890/12,702,379 prevalence, 17.24%) were overrepresented on Twitter relative to disease prevalence, whereas hypertension (17,245/174,381 messages, 9.89%; 4,614,776/12,702,379 prevalence, 36.33%), chronic obstructive pulmonary disease (1648/174,381 messages, 0.95%; 1,083,627/12,702,379 prevalence, 8.53%), and heart disease (13,669/174,381 messages, 7.84%; 2,461,721/12,702,379 prevalence, 19.38%) were underrepresented. The content of messages also varied by disease. Personal experience messages accounted for 12.88% (578/4487) of prostate cancer tweets and 24.17% (4046/16,742) of asthma tweets. Awareness-themed tweets were more often about breast cancer (9139/39,156 messages, 23.34%) than asthma (1040/16,742 messages, 6.21%). Tweets about risk factors were more often about heart disease (1375/13,669 messages, 10.06%) than lymphoma (105/4927 messages, 2.13%).

Conclusions

Twitter provides a window into the Web-based visibility of diseases and how the volume of Web-based content about diseases varies by condition. Further, the potential value in tweets is in the rich content they provide about individuals’ perspectives about diseases (eg, personal experiences, awareness, and risk factors) that are not otherwise easily captured through traditional surveys or administrative data.

Digital Technology and Mobile Health in Behavioral Migraine Therapy: a Narrative Review

PURPOSE OF REVIEW

This article reviews the recent research and development of electronic health (eHealth) and, in particular, mobile health (mHealth) strategies to deliver behavioral treatment for migraine. Prospects for future development and research of mobile health in migraine are suggested.

RECENT FINDINGS

Advances in digital technology and mobile technology have led to an era where electronic and mobile approaches are applied to several aspects of healthcare. Electronic behavioral interventions for migraine seem to be acceptable and feasible, but efficacy measures are uncertain. Clinical trials on mHealth-based classical behavioral therapies, such as relaxation, biofeedback, and cognitive behavioral therapy are missing in the literature. Within mHealth, headache diaries are the most researched and scientifically developed. Still, there is a gap between commercially available apps and scientifically validated and developed apps. Digital technology and mobile health has not yet lived out its potential in behavioral migraine therapy. Application of proper usability and functionality designs towards the right market, together with appraisal of medical and technological recommendations, may facilitate rapid development of eHealth and mHealth, while also establishing scientific evidence.

Identifying personal health experience tweets with deep neural networks

Twitter, as a social media platform, has become an increasingly useful data source for health surveillance studies, and personal health experiences shared on Twitter provide valuable information to the surveillance. Twitter data are known for their irregular usages of languages and informal short texts due to the 140 character limit, and for their noisiness such that majority of the posts are irrelevant to any particular health surveillance. These factors pose challenges in identifying personal health experience tweets from the Twitter data. In this study, we designed deep neural networks with 3 different architectural configurations, and after training them with a corpus of 8,770 annotated tweets, we used them to predict personal experience tweets from a set of 821 annotate tweets. Our results demonstrated a significant amount of improvement in predicting personal health experience tweets by deep neural networks over that by conventional classifiers: 37.5% in accuracy, 31.1% in precision, and 53.6% in recall. We believe that our method can be utilized in various health surveillance studies using Twitter as a data source.

Trends of infodemiology studies: a scoping review

INTRODUCTION

The health care industry is rich in data and information. Web technologies, such as search engines and social media, have provided an opportunity for the management of user generated data in real time in the form of infodemiology studies. The aim of this study was to investigate infodemiology studies conducted during 2002-2016, and compare them based on developed, developing and in transition countries.

METHODS

This scoping review was conducted in 2017 with the help of the PRISMA guidelines. PubMed, Scopus, Science Direct, Web of Knowledge, Google Scholar, Wiley and Springer databases were searched between the years 2002 and 2016. Finally, 56 articles were included in the review and analysed.

RESULTS

The initial infodemiology studies pertain to the quality assessment of the hospital's websites. Most of the studies were on developed countries, based on flu, and published in the Journal of Medical Internet Research.

CONCLUSION

The infodemiology approach provides unmatched opportunities for the management of health data and information generated by the users. Using this potential will provide unique opportunities for the health information need assessment in real time by health librarians and thereby provide evidence based health information to the people.

Social Media as a Catalyst for Policy Action and Social Change for Health and Well-Being: Viewpoint

This viewpoint paper argues that policy interventions can benefit from the continued use of social media analytics, which can serve as an important complement to traditional social science data collection and analysis. Efforts to improve well-being should provide an opportunity to explore these areas more deeply, and encourage the efforts of those conducting national and local data collection on health to incorporate more of these emerging data sources.

Social media remains a relatively untapped source of information to catalyze policy action and social change. However, the diversity of social media platforms and available analysis techniques provides multiple ways to offer insight for policy making and decision making. For instance, social media content can provide timely information about the impact of policy interventions. Social media location information can inform where to deploy resources or disseminate public messaging. Network analysis of social media connections can reveal underserved populations who may be disconnected from public services. Machine learning can help recognize important patterns for disease surveillance or to model population sentiment. To fully realize these potential policy uses, limitations to social media data will need to be overcome, including data reliability and validity, and potential privacy risks.

Traditional data collection may not fully capture the upstream factors and systemic relationships that influence health and well-being. Policy actions and social change efforts, such as the Robert Wood Johnson Foundation’s effort to advance a culture of health, which are intended to drive change in a network of upstream health drivers, will need to incorporate a broad range of behavioral information, such as health attitudes or physical activity levels. Applying innovative techniques to emerging data has the potential to extract insight from unstructured data or fuse disparate sources of data, such as linking health attitudes that are expressed to health behaviors or broader health and well-being outcomes.

Clinical description of attack-related cognitive symptoms in migraine: A systematic review

Introduction

Cognitive symptoms have been described during migraine attacks since the Roman era; while being neglected throughout the centuries, they are relevant contributors to migraine-related disability.

Objective

To determine whether cognitive symptoms are included in clinical series describing migraine attack phenomenology, and which symptoms occur in each attack phase.

Method

Systematic review of existing data on clinical descriptions of migraine attacks, focusing on cognitive symptomatology. Data were organized and analyzed qualitatively, due to methodological differences between studies.

Results

Twenty-four articles were reviewed, with a total sample of 7007 patients, including 82.9% females with an average age of 39.2 years. Twenty one (75%) studies analyzed one phase of the attack (eight prodromes, five auras, one between aura and pain, three headaches and three postdromes), the remaining studied more than one phase. Cognitive complaints were the most frequent symptom of the prodromic (30%) and headache (38%) phases, while fatigue (70%) dominated the resolution phase. Not enough data is available to estimate the frequency of cognitive symptoms during the aura.

Discussion

Cognitive symptoms are described in all phases of the migraine attack phenomenology in published clinical series of migraine. Their characteristics appear to be different in each attack phase, although methodological limitations prevent generalization of this finding.

Epidemiology from Tweets: Estimating Misuse of Prescription Opioids in the USA from Social Media

BACKGROUND

The misuse of prescription opioids (MUPO) is a leading public health concern. Social media are playing an expanded role in public health research, but there are few methods for estimating established epidemiological metrics from social media. The purpose of this study was to demonstrate that the geographic variation of social media posts mentioning prescription opioid misuse strongly correlates with government estimates of MUPO in the last month.

METHODS

We wrote software to acquire publicly available tweets from Twitter from 2012 to 2014 that contained at least one keyword related to prescription opioid use (n = 3,611,528). A medical toxicologist and emergency physician curated the list of keywords. We used the semantic distance (SemD) to automatically quantify the similarity of meaning between tweets and identify tweets that mentioned MUPO. We defined the SemD between two words as the shortest distance between the two corresponding word-centroids. Each word-centroid represented all recognized meanings of a word. We validated this automatic identification with manual curation. We used Twitter metadata to estimate the location of each tweet. We compared our estimated geographic distribution with the 2013-2015 National Surveys on Drug Usage and Health (NSDUH).

RESULTS

Tweets that mentioned MUPO formed a distinct cluster far away from semantically unrelated tweets. The state-by-state correlation between Twitter and NSDUH was highly significant across all NSDUH survey years. The correlation was strongest between Twitter and NSDUH data from those aged 18-25 (r = 0.94, p < 0.01 for 2012; r = 0.94, p < 0.01 for 2013; r = 0.71, p = 0.02 for 2014). The correlation was driven by discussions of opioid use, even after controlling for geographic variation in Twitter usage.

CONCLUSIONS

Mentions of MUPO on Twitter correlate strongly with state-by-state NSDUH estimates of MUPO. We have also demonstrated that a natural language processing can be used to analyze social media to provide insights for syndromic toxicosurveillance.

Using Search Engines to Investigate Shared Migraine Experiences

OBJECTIVES

To investigate migraine patterns in the United States using Google search data and utilize this information to better understand societal-level trends. Additionally, we aimed to evaluate time-series relationships between migraines and social factors.

BACKGROUND

Extensive research has been done on clinical factors associated with migraines, yet population-level social factors have not been widely explored. Migraine internet search data may provide insight into migraine trends beyond information that can be gleaned from other sources.

METHODS

In this longitudinal analysis of open access data, we performed a time-series analysis in which about 12 years of Google Trends data (January 1, 2004 to August 15, 2016) were assessed. Data points were captured at a daily level and Google's 0-100 adjusted scale was used as the primary outcome to enable the comparison of relative popularity in the migraine search term. We hypothesized that the volume of relative migraine Google searches would be affected by societal aspects such as day of the week, holidays, and novel social events.

RESULTS

Several recurrent social factors that drive migraine searches were identified. Of these, day of the week had the most significant impact on the volume of Google migraine searches. On average, Mondays accumulated 13.31 higher relative search volume than Fridays (95% CI: 11.12-15.51, P ≤ .001). Surprisingly, holidays were associated with lower relative migraine search volumes. Christmas Day had 13.84 lower relative search volumes (95% CI: 6.26-21.43, P ≤ .001) and Thanks giving had 20.18 lower relative search volumes (95% CI: 12.55-27.82, P ≤ .001) than days that were not holidays. Certain novel social events and extreme weather also appear to be associated with relative migraine Google search volume.

CONCLUSIONS

Social factors play a crucial role in explaining population level migraine patterns, and thus, warrant further exploration.

Twitter analysis of the orthodontic patient experience with braces vs Invisalign

OBJECTIVE

To examine the orthodontic patient experience having braces compared with Invisalign by means of a large-scale Twitter sentiment analysis.

MATERIALS AND METHODS

A custom data collection program was created that collected tweets containing the words "braces" or "Invisalign" for a period of 5 months. A hierarchal Naïve Bayes sentiment analysis classifier was developed to sort the tweets into five categories: positive, negative, neutral, advertisement, or not applicable. Each category was then analyzed for specific content.

RESULTS

A total of 419,363 tweets applicable to orthodontics were collected. Users posted significantly more positive tweets (61%) than they did negative tweets (39%; P ≤ .0001). There was no significant difference in the distribution of positive and negative sentiment between braces and Invisalign tweets (P = .4189). Positive orthodontics-related tweets often highlighted gratitude for a great smile accompanied with selfies. Negative orthodontic tweets frequently focused on pain.

CONCLUSION

Twitter users expressed more positive than negative sentiment about orthodontic treatment with no significant difference in sentiment between braces and Invisalign tweets.

Revisiting the connection between Solar eruptions and primary headaches and migraines using Twitter

Many internal and environmental triggers of primary headaches have been proposed, but establishing firm evidence for any of them has proved elusive. Geomagnetic storms, the disturbances of Earth’s magnetic field following Solar eruptions, have been proposed as one such trigger. In this study, we utilized a vast amount of self-reported symptoms from the online social networking service Twitter in order to investigate a purported link between the level of geomagnetic activity and the onset of primary headaches and migraines. We analyzed 63 million keyword-bearing messages posted over the three years covering the maximum of Solar Cycle 24. No correlation has been found despite the large sample size. The simulation reveals that the significant correlation would have emerged even if only 1% of headache (2% of migraine) instances were caused by geomagnetic disturbances, thus placing very low upper limits on the prevalence of this trigger among Twitter users.

The migraine generator revisited: continuous scanning of the migraine cycle over 30 days and three spontaneous attacks

Functional imaging using positron emission tomography and later functional magnetic resonance imaging revealed a particular brainstem area that is believed to be specifically activated in migraine during, but not outside of the attack, and consequently has been coined the 'migraine generator'. However, the pathophysiological concept behind this term is not undisputed and typical migraine premonitory symptoms such as fatigue and yawning, but also a typical association of attacks to circadian and menstrual cycles, all make the hypothalamus a possible regulating region of migraine attacks. Neuroimaging studies investigating native human migraine attacks however are scarce and for methodological but also clinical reasons there are currently no studies investigating the last 24 h before headache onset. Here we report a migraine patient who had magnetic resonance imaging every day for 30 days, always in the morning, to cover, using functional imaging, a whole month and three complete, untreated migraine attacks. We found that hypothalamic activity as a response to trigeminal nociceptive stimulation is altered during the 24 h prior to pain onset, i.e. increases towards the next migraine attack. More importantly, the hypothalamus shows altered functional coupling with the spinal trigeminal nuclei and the region of the migraine generator, i.e. the dorsal rostral pons during the preictal day and the pain phase of native human migraine attacks. These data suggest that although the brainstem is highly linked to the migraine biology, the real driver of attacks might be the functional changes in hypothalamo-brainstem connectivity.

2015 American Journal of Gastroenterology Lecture: How Digital Health Will Transform Gastroenterology

Our patients spend most of their lives far away from an examination room. If we are truly going to capture our patients' attention and engage them in their care, then we must reach beyond the four walls of the clinic, hospital, or endoscopy suite. This is the vision of the digital health movement-an effort to monitor patients remotely and dynamically with mobile health ("mHealth") smartphone applications, electronic health record portals, social media, and wearable biosensors to improve health care outside of the clinical trenches. This article explores how advances in digital health may improve health-care delivery, focusing on gastroenterology and hepatology. It describes how technology can monitor patients remotely, improve face-to-face care, drive clinical decisions, and offer value to health-care organizations, their patients, and their staff. The article also describes pitfalls and shortcomings of digital technologies and concludes by describing a new model for how digital health can be deployed at scale to improve coordination and outcomes of care.

Social Media Analyses for Social Measurement

Demonstrations that analyses of social media content can align with measurement from sample surveys have raised the question of whether survey research can be supplemented or even replaced with less costly and burdensome data mining of already-existing or "found" social media content. But just how trustworthy such measurement can be-say, to replace official statistics-is unknown. Survey researchers and data scientists approach key questions from starting assumptions and analytic traditions that differ on, for example, the need for representative samples drawn from frames that fully cover the population. New conversations between these scholarly communities are needed to understand the potential points of alignment and non-alignment. Across these approaches, there are major differences in (a) how participants (survey respondents and social media posters) understand the activity they are engaged in; (b) the nature of the data produced by survey responses and social media posts, and the inferences that are legitimate given the data; and (c) practical and ethical considerations surrounding the use of the data. Estimates are likely to align to differing degrees depending on the research topic and the populations under consideration, the particular features of the surveys and social media sites involved, and the analytic techniques for extracting opinions and experiences from social media. Traditional population coverage may not be required for social media content to effectively predict social phenomena to the extent that social media content distills or summarizes broader conversations that are also measured by surveys.

A Twitter Education: Why Psychiatrists Should Tweet

Social media tools such as blogs, microblogs, social networking sites, podcasts, and video-sharing sites are now ubiquitous. These tools enable instantaneous interactions with a global community of individuals, including medical professionals, learners, and patients. An understanding of social media tools and how they can be used by psychiatrists is increasingly important. This review defines some relevant social media terms and addresses challenges specific to the use of social media in psychiatry. Focused primarily on Twitter, one of the most commonly used social media tools, the review describes how Twitter is being used in non-psychiatric medical fields and highlights four current and/or potential uses of Twitter in psychiatry: (1) patient care and advocacy, (2) lifelong learning, (3) research data collection and collaboration, and (4) scholarly recognition and impact.

Assessment of the Quality of Patient-Orientated Information on Surgery for Crohn's Disease on the Internet

BACKGROUND

The Internet is a vast resource for patients to search for health information on the treatment of Crohn's disease.

OBJECTIVE

This study examines the quality of Web sites that provide information to adults regarding Crohn's disease, including treatment options and surgery.

DESIGN

Two search engines (Google and Yahoo) and the search terms "surgery for Crohn's disease" were used. The first 50 sites of each search were assessed. Sites that fulfilled the inclusion criteria were evaluated for content and scored by using the DISCERN instrument, which evaluates the quality of health information on treatment choices.

RESULTS

One hundred sites were examined, of which 13 were duplicates. Sixty-two sites provided patient-orientated information. The other sites included 7 scientific articles, 3 blogs, 2 links, 6 forums, 3 video links, and 4 dead links. Of the 62 Web sites that provided patient information for adults, only 15 (24.2%) had been updated within the past 2 years. Only 9 (14.5%) were affiliated with hospitals and clinics. The majority of sites (33, 53.2%) were associated with private companies with commercial interests. Only half of the Web sites provided details on treatment options, and most Web sites did not provide any information on symptoms and procedure details. Just 5 Web sites (8.1%) described the risks of surgery, and only 7 (11.3%) provided any information on the timescale for recovery. Overall, only 1 Web site (1.6%) was identified as being "good" or "excellent" with the use of the DISCERN criteria.

LIMITATIONS

Although the internet is constantly evolving, this study captures data at a specific time point. Search results may vary depending on geographical location. This study only assessed English language websites.

CONCLUSIONS

The quality of patient information on surgery for Crohn's disease is highly variable and generally poor. There is potential for the Internet to provide valuable information, and clinicians should identify high-quality Web sites to guide their patients.

Using Twitter to Measure Public Discussion of Diseases: A Case Study

Background

Twitter is increasingly used to estimate disease prevalence, but such measurements can be biased, due to both biased sampling and inherent ambiguity of natural language.

Objective

We characterized the extent of these biases and how they vary with disease.

Methods

We correlated self-reported prevalence rates for 22 diseases from Experian’s Simmons National Consumer Study (n=12,305) with the number of times these diseases were mentioned on Twitter during the same period (2012). We also identified and corrected for two types of bias present in Twitter data: (1) demographic variance between US Twitter users and the general US population; and (2) natural language ambiguity, which creates the possibility that mention of a disease name may not actually refer to the disease (eg, “heart attack” on Twitter often does not refer to myocardial infarction). We measured the correlation between disease prevalence and Twitter disease mentions both with and without bias correction. This allowed us to quantify each disease’s overrepresentation or underrepresentation on Twitter, relative to its prevalence.

Results

Our sample included 80,680,449 tweets. Adjusting disease prevalence to correct for Twitter demographics more than doubles the correlation between Twitter disease mentions and disease prevalence in the general population (from .113 to .258, P <.001). In addition, diseases varied widely in how often mentions of their names on Twitter actually referred to the diseases, from 14.89% (3827/25,704) of instances (for stroke) to 99.92% (5044/5048) of instances (for arthritis). Applying ambiguity correction to our Twitter corpus achieves a correlation between disease mentions and prevalence of .208 ( P <.001). Simultaneously applying correction for both demographics and ambiguity more than triples the baseline correlation to .366 ( P <.001). Compared with prevalence rates, cancer appeared most overrepresented in Twitter, whereas high cholesterol appeared most underrepresented.

Conclusions

Twitter is a potentially useful tool to measure public interest in and concerns about different diseases, but when comparing diseases, improvements can be made by adjusting for population demographics and word ambiguity.

The reliability of tweets as a supplementary method of seasonal influenza surveillance

BACKGROUND

Existing influenza surveillance in the United States is focused on the collection of data from sentinel physicians and hospitals; however, the compilation and distribution of reports are usually delayed by up to 2 weeks. With the popularity of social media growing, the Internet is a source for syndromic surveillance due to the availability of large amounts of data. In this study, tweets, or posts of 140 characters or less, from the website Twitter were collected and analyzed for their potential as surveillance for seasonal influenza.

OBJECTIVE

There were three aims: (1) to improve the correlation of tweets to sentinel-provided influenza-like illness (ILI) rates by city through filtering and a machine-learning classifier, (2) to observe correlations of tweets for emergency department ILI rates by city, and (3) to explore correlations for tweets to laboratory-confirmed influenza cases in San Diego.

METHODS

Tweets containing the keyword "flu" were collected within a 17-mile radius from 11 US cities selected for population and availability of ILI data. At the end of the collection period, 159,802 tweets were used for correlation analyses with sentinel-provided ILI and emergency department ILI rates as reported by the corresponding city or county health department. Two separate methods were used to observe correlations between tweets and ILI rates: filtering the tweets by type (non-retweets, retweets, tweets with a URL, tweets without a URL), and the use of a machine-learning classifier that determined whether a tweet was "valid", or from a user who was likely ill with the flu.

RESULTS

Correlations varied by city but general trends were observed. Non-retweets and tweets without a URL had higher and more significant (P<.05) correlations than retweets and tweets with a URL. Correlations of tweets to emergency department ILI rates were higher than the correlations observed for sentinel-provided ILI for most of the cities. The machine-learning classifier yielded the highest correlations for many of the cities when using the sentinel-provided or emergency department ILI as well as the number of laboratory-confirmed influenza cases in San Diego. High correlation values (r=.93) with significance at P<.001 were observed for laboratory-confirmed influenza cases for most categories and tweets determined to be valid by the classifier.

CONCLUSIONS

Compared to tweet analyses in the previous influenza season, this study demonstrated increased accuracy in using Twitter as a supplementary surveillance tool for influenza as better filtering and classification methods yielded higher correlations for the 2013-2014 influenza season than those found for tweets in the previous influenza season, where emergency department ILI rates were better correlated to tweets than sentinel-provided ILI rates. Further investigations in the field would require expansion with regard to the location that the tweets are collected from, as well as the availability of more ILI data.

Imaging sensory effects of occipital nerve stimulation: a new computer-based method in neuromodulation

BACKGROUND

Within the last years, occipital nerve stimulation (ONS) has proven to be an important method in the treatment of severe therapy-resistant neurological pain disorders. The correspondence between lead placement as well as possible stimulation parameters and the resulting stimulation effects remains unclear.

OBJECTIVE

The method aims to directly relate the neuromodulatory mechanisms with the clinical treatment results, to achieve insight in the mode of action of neuromodulation, to identify the most effective stimulation sets and to optimize individual treatment effects.

METHODS

We describe a new computer-based imaging method for mapping the spatial, cognitive and affective sensory effects of ONS. The procedure allows a quantitative and qualitative analysis of the relationship between lead positioning, the stimulation settings as well as the sensory and clinical stimulation effects.

CONCLUSION

A regular mapping of stimulation and sensory parameters allows a coordinated monitoring. The stimulation results can be reviewed and compared with regards to clinical effectiveness.