Twitter Sentiment Predicts Affordable Care Act Marketplace Enrollment

Background

Traditional metrics of the impact of the Affordable Care Act (ACA) and health insurance marketplaces in the United States include public opinion polls and marketplace enrollment, which are published with a lag of weeks to months. In this rapidly changing environment, a real-time barometer of public opinion with a mechanism to identify emerging issues would be valuable.

Objective

We sought to evaluate Twitter’s role as a real-time barometer of public sentiment on the ACA and to determine if Twitter sentiment (the positivity or negativity of tweets) could be predictive of state-level marketplace enrollment.

Methods

We retrospectively collected 977,303 ACA-related tweets in March 2014 and then tested a correlation of Twitter sentiment with marketplace enrollment by state.

Results

A 0.10 increase in the sentiment score was associated with an 8.7% increase in enrollment at the state level (95% CI 1.32-16.13; P=.02), a correlation that remained significant when adjusting for state Medicaid expansion (P=.02) or use of a state-based marketplace (P=.03).

Conclusions

This correlation indicates Twitter’s potential as a real-time monitoring strategy for future marketplace enrollment periods; marketplaces could systematically track Twitter sentiment to more rapidly identify enrollment changes and potentially emerging issues. As a repository of free and accessible consumer-generated opinions, this study reveals a novel role for Twitter in the health policy landscape.     

Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products

Background

Social media platforms such as Twitter are rapidly becoming key resources for public health surveillance applications, yet little is known about Twitter users’ levels of informedness and sentiment toward tobacco, especially with regard to the emerging tobacco control challenges posed by hookah and electronic cigarettes.

Objective

To develop a content and sentiment analysis of tobacco-related Twitter posts and build machine learning classifiers to detect tobacco-relevant posts and sentiment towards tobacco, with a particular focus on new and emerging products like hookah and electronic cigarettes.

Methods

We collected 7362 tobacco-related Twitter posts at 15-day intervals from December 2011 to July 2012. Each tweet was manually classified using a triaxial scheme, capturing genre, theme, and sentiment. Using the collected data, machine-learning classifiers were trained to detect tobacco-related vs irrelevant tweets as well as positive vs negative sentiment, using Naïve Bayes, k-nearest neighbors, and Support Vector Machine (SVM) algorithms. Finally, phi contingency coefficients were computed between each of the categories to discover emergent patterns.

Results

The most prevalent genres were first- and second-hand experience and opinion, and the most frequent themes were hookah, cessation, and pleasure. Sentiment toward tobacco was overall more positive (1939/4215, 46% of tweets) than negative (1349/4215, 32%) or neutral among tweets mentioning it, even excluding the 9% of tweets categorized as marketing. Three separate metrics converged to support an emergent distinction between, on one hand, hookah and electronic cigarettes corresponding to positive sentiment, and on the other hand, traditional tobacco products and more general references corresponding to negative sentiment. These metrics included correlations between categories in the annotation scheme (phi_{hookah-positive}=0.39; phi_{e-cigs-positive}=0.19); correlations between search keywords and sentiment (χ² ₄=414.50, P<.001, Cramer’s V=0.36), and the most discriminating unigram features for positive and negative sentiment ranked by log odds ratio in the machine learning component of the study. In the automated classification tasks, SVMs using a relatively small number of unigram features (500) achieved best performance in discriminating tobacco-related from unrelated tweets (F score=0.85).

Conclusions

Novel insights available through Twitter for tobacco surveillance are attested through the high prevalence of positive sentiment. This positive sentiment is correlated in complex ways with social image, personal experience, and recently popular products such as hookah and electronic cigarettes. Several apparent perceptual disconnects between these products and their health effects suggest opportunities for tobacco control education. Finally, machine classification of tobacco-related posts shows a promising edge over strictly keyword-based approaches, yielding an improved signal-to-noise ratio in Twitter data and paving the way for automated tobacco surveillance applications.     

A Scalable Framework to Detect Personal Health Mentions on Twitter

Background

Biomedical research has traditionally been conducted via surveys and the analysis of medical records. However, these resources are limited in their content, such that non-traditional domains (eg, online forums and social media) have an opportunity to supplement the view of an individual’s health.

Objective

The objective of this study was to develop a scalable framework to detect personal health status mentions on Twitter and assess the extent to which such information is disclosed.

Methods

We collected more than 250 million tweets via the Twitter streaming API over a 2-month period in 2014. The corpus was filtered down to approximately 250,000 tweets, stratified across 34 high-impact health issues, based on guidance from the Medical Expenditure Panel Survey. We created a labeled corpus of several thousand tweets via a survey, administered over Amazon Mechanical Turk, that documents when terms correspond to mentions of personal health issues or an alternative (eg, a metaphor). We engineered a scalable classifier for personal health mentions via feature selection and assessed its potential over the health issues. We further investigated the utility of the tweets by determining the extent to which Twitter users disclose personal health status.

Results

Our investigation yielded several notable findings. First, we find that tweets from a small subset of the health issues can train a scalable classifier to detect health mentions. Specifically, training on 2000 tweets from four health issues (cancer, depression, hypertension, and leukemia) yielded a classifier with precision of 0.77 on all 34 health issues. Second, Twitter users disclosed personal health status for all health issues. Notably, personal health status was disclosed over 50% of the time for 11 out of 34 (33%) investigated health issues. Third, the disclosure rate was dependent on the health issue in a statistically significant manner (P<.001). For instance, more than 80% of the tweets about migraines (83/100) and allergies (85/100) communicated personal health status, while only around 10% of the tweets about obesity (13/100) and heart attack (12/100) did so. Fourth, the likelihood that people disclose their own versus other people’s health status was dependent on health issue in a statistically significant manner as well (P<.001). For example, 69% (69/100) of the insomnia tweets disclosed the author’s status, while only 1% (1/100) disclosed another person’s status. By contrast, 1% (1/100) of the Down syndrome tweets disclosed the author’s status, while 21% (21/100) disclosed another person’s status.

Conclusions

It is possible to automatically detect personal health status mentions on Twitter in a scalable manner. These mentions correspond to the health issues of the Twitter users themselves, but also other individuals. Though this study did not investigate the veracity of such statements, we anticipate such information may be useful in supplementing traditional health-related sources for research purposes.     

What Are Health-Related Users Tweeting? A Qualitative Content Analysis of Health-Related Users and Their Messages on Twitter

Background

Twitter is home to many health professionals who send messages about a variety of health-related topics. Amid concerns about physicians posting inappropriate content online, more in-depth knowledge about these messages is needed to understand health professionals’ behavior on Twitter.

Objective

Our goal was to characterize the content of Twitter messages, specifically focusing on health professionals and their tweets relating to health.

Methods

We performed an in-depth content analysis of 700 tweets. Qualitative content analysis was conducted on tweets by health users on Twitter. The primary objective was to describe the general type of content (ie, health-related versus non-health related) on Twitter authored by health professionals and further to describe health-related tweets on the basis of the type of statement made. Specific attention was given to whether a tweet was personal (as opposed to professional) or made a claim that users would expect to be supported by some level of medical evidence (ie, a “testable” claim). A secondary objective was to compare content types among different users, including patients, physicians, nurses, health care organizations, and others.

Results

Health-related users are posting a wide range of content on Twitter. Among health-related tweets, 53.2% (184/346) contained a testable claim. Of health-related tweets by providers, 17.6% (61/346) were personal in nature; 61% (59/96) made testable statements. While organizations and businesses use Twitter to promote their services and products, patient advocates are using this tool to share their personal experiences with health.

Conclusions

Twitter users in health-related fields tweet about both testable claims and personal experiences. Future work should assess the relationship between testable tweets and the actual level of evidence supporting them, including how Twitter users—especially patients—interpret the content of tweets posted by health providers.     

Analysis of the Purpose of State Health Departments' Tweets: Information Sharing,Engagement, and Action

Background

Public health agencies are actively using social media, including Twitter. In the public health and nonprofit sectors, Twitter has been limited to one-way communication. Two-way, interactive communication on Twitter has the potential to enhance organizational relationships with followers and help organizations achieve their goals by increasing communication and dialog between the organization and its followers. Research shows that nonprofit organizations use Twitter for three main functions: information sharing, community building, and action.

Objective

It is not known whether state health departments are using Twitter primarily for one-way information sharing or if they are trying to engage followers to build relationships and promote action. The purpose of this research was to discover what the primary function of Twitter use is among state health departments in the United States and whether this is similar to or different from nonprofit organizations.

Methods

A complete list of “tweets” made by each state health department account was obtained using the Twitter application programming interface. We randomly sampled 10% of each state health department’s tweets. Four research assistants hand-coded the tweets’ primary focus (organization centric or personal health information centric) and then the subcategories of information dissemination, engagement, or action. Research assistants coded each tweet for interactivity, sophistication, and redirects to another website. Data were analyzed using SPSS version 20.

Results

There were 4221 tweets from 39 state health departments. There was no statistically significant difference in the number of tweets made by a state health department and the state population density (P=.25). The majority of tweets focused on personal health topics (69.37%, 2928/4221) while one-third were tweets about the organization (29.14% , 1230/4221). The main function of organization-based tweets was engagement through conversations to build community (65.77%, 809/1236). These engagement-related tweets were primarily recognition of other organizations’ events (43.6%, 353/809) and giving thanks and recognition (21.4%, 173/809). Nearly all of the personal health information-centric tweets involved general public health information (92.10%, 1399/1519) and 79.03% (3336/4221) of tweets directed followers to another link for more information.

Conclusions

This is the first study to assess the purpose of public health tweets among state health departments. State health departments are using Twitter as a one-way communication tool, with tweets focused primarily on personal health. A state health department Twitter account may not be the primary health information source for individuals. Therefore, state health departments should reconsider their focus on personal health tweets and envision how they can use Twitter to develop relationships with community agencies and partners. In order to realize the potential of Twitter to establish relationships and develop connections, more two-way communication and interaction are essential.     

Use of Twitter Among Local Health Departments: An Analysis of Information Sharing,Engagement, and Action

Background

Social media offers unprecedented opportunities for public health to engage audiences in conversations and collaboration that could potentially lead to improved health conditions. While there is some evidence that local health departments (LHDs) are using social media and Twitter in particular, little is known about how Twitter is used by LHDs and how they use it to engage followers versus disseminating one-way information.

Objective

To examine how LHDs use Twitter to share information, engage with followers, and promote action, as well as to discover differences in Twitter use among LHDs by size of population served.

Methods

The Twitter accounts for 210 LHDs were stratified into three groups based on size of population served (n=69 for less than 100,000; n=89 for 100,000-499,999; n=52 for 500,000 or greater). A sample of 1000 tweets was obtained for each stratum and coded as being either about the organization or about personal-health topics. Subcategories for organization included information, engagement, and action. Subcategories for personal health included information and action.

Results

Of all LHD tweets (n=3000), 56.1% (1682/3000) related to personal health compared with 39.5% (1186/3000) that were about the organization. Of the personal-health tweets, 58.5% (984/1682) involved factual information and 41.4% (697/1682) encouraged action. Of the organization-related tweets, 51.9% (615/1186) represented one-way communication about the organization and its events and services, 35.0% (416/1186) tried to engage followers in conversation, and 13.3% (158/1186) encouraged action to benefit the organization (eg, attend events). Compared with large LHDs, small LHDs were more likely to post tweets about their organization (Cramer’s V=0.06) but were less likely to acknowledge events and accomplishments of other organizations (χ²=12.83, P=.02, Cramer’s V=0.18). Small LHDs were also less likely to post personal health-related tweets (Cramer’s V=0.08) and were less likely to post tweets containing suggestions to take action to modify their lifestyle. While large LHDs were more likely to post engagement-related tweets about the organization (Cramer’s V=0.12), they were less likely to ask followers to take action that would benefit the organization (χ²=7.59, P=.02. Cramer’s V=0.08). While certain associations were statistically significant, the Cramer’s V statistic revealed weak associations.

Conclusions

Twitter is being adopted by LHDs, but its primary use involves one-way communication on personal-health topics as well as organization-related information. There is also evidence that LHDs are starting to use Twitter to engage their audiences in conversations. As public health transitions to more dialogic conversation and engagement, Twitter’s potential to help form partnerships with audiences and involve them as program participants may lead to action for improved health.     

Characterizing the Followers and Tweets of a Marijuana-Focused Twitter Handle

Background

Twitter is a popular social media forum for sharing personal experiences, interests, and opinions. An improved understanding of the discourse on Twitter that encourages marijuana use can be helpful for tailoring and targeting online and offline prevention messages.

Objectives

The intent of the study was to assess the content of “tweets” and the demographics of followers of a popular pro-marijuana Twitter handle (@stillblazingtho).

Methods

We assessed the sentiment and content of tweets (sent from May 1 to December 31, 2013), as well as the demographics of consumers that follow a popular pro-marijuana Twitter handle (approximately 1,000,000 followers) using Twitter analytics from Demographics Pro. This analytics company estimates demographic characteristics based on Twitter behavior/usage, relying on multiple data signals from networks, consumption, and language and requires confidence of 95% or above to make an estimate of a single demographic characteristic.

Results

A total of 2590 tweets were sent from @stillblazingtho during the 8-month period and 305 (11.78%) replies to another Twitter user were excluded for qualitative analysis. Of the remaining 2285 tweets, 1875 (82.06%) were positive about marijuana, 403 (17.64%) were neutral, and 7 (0.31%) appeared negative about marijuana. Approximately 1101 (58.72%) of the positive marijuana tweets were perceived as jokes or humorous, 340 (18.13%) implied that marijuana helps you to feel good or relax, 294 (15.68%) mentioned routine, frequent, or heavy use, 193 (10.29%) mentioned blunts, marijuana edibles, or paraphernalia (eg, bongs, vaporizers), and 186 (9.92%) mentioned other risky health behaviors (eg, tobacco, alcohol, other drugs, sex). The majority (699,103/959,143; 72.89%) of @stillblazingtho followers were 19 years old or younger. Among people ages 17 to 19 years, @stillblazingtho was in the top 10% of all Twitter handles followed. More followers of @stillblazingtho in the United States were African American (323,107/759,407; 42.55%) or Hispanic (90,732/759,407; 11.95%) than the Twitter median average (African American 22.4%, inter-quartile ratio [IQR] 5.1-62.5%; Hispanic 5.4%, IQR 3.0-10.8%) and among Hispanics, @stillblazingtho was in the top 30% of all Twitter handles followed.

Conclusions

Young people are especially responsive to social media influences and often establish substance use patterns during this phase of development. Our findings underscore the need for surveillance efforts to monitor the pro-marijuana content reaching young people on Twitter.     

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.     

Sleep and Depression in Postpartum Women: A Population-Based Study

Study Objectives:

(1) To describe the prevalence of and risk factors for postpartum maternal sleep problems and depressive symptoms simultaneously, (2) identify factors independently associated with either condition, and (3) explore associations between specific postpartum sleep components and depression.

Design:

Cross-sectional.

Setting:

Population-based.

Participants:

All women (n = 4191) who had delivered at Stavanger University Hospital from October 2005 to September 2006 were mailed a questionnaire seven weeks postpartum. The response rate was 68% (n = 2830).

Interventions:

None.

Measurements and results:

Sleep was measured using the Pittsburgh Sleep Quality Index (PSQI), and depressive symptoms using the Edinburgh Postnatal Depression Scale (EPDS). The prevalence of sleep problems, defined as PSQI > 5, was 57.7%, and the prevalence of depression, defined as EPDS ≥ 10, was 16.5%. The mean self-reported nightly sleep duration was 6.5 hours and sleep efficiency 73%. Depression, previous sleep problems, being primiparous, not exclusively breastfeeding, or having a younger or male infant were factors associated with poor postpartum sleep quality. Poor sleep was also associated with depression when adjusted for other significant risk factors for depression, such as poor partner relationship, previous depression, depression during pregnancy and stressful life events. Sleep disturbances and subjective sleep quality were the aspects of sleep most strongly associated with depression.

Conclusions:

Poor sleep was associated with depression independently of other risk factors. Poor sleep may increase the risk of depression in some women, but as previously known risk factors were also associated, mothers diagnosed with postpartum depression are not merely reporting symptoms of chronic sleep deprivation.

Citation:

Dørheim SK; Bondevik GT; Eberhard-Gran M; Bjorvatn B. Sleep and depression in postpartum women: a population-based study. SLEEP 2009;32(7):847-855.     

Associations Between Exposure to and Expression of Negative Opinions About Human Papillomavirus Vaccines on Social Media: An Observational Study

Background

Groups and individuals that seek to negatively influence public opinion about the safety and value of vaccination are active in online and social media and may influence decision making within some communities.

Objective

We sought to measure whether exposure to negative opinions about human papillomavirus (HPV) vaccines in Twitter communities is associated with the subsequent expression of negative opinions by explicitly measuring potential information exposure over the social structure of Twitter communities.

Methods

We hypothesized that prior exposure to opinions rejecting the safety or value of HPV vaccines would be associated with an increased risk of posting similar opinions and tested this hypothesis by analyzing temporal sequences of messages posted on Twitter (tweets). The study design was a retrospective analysis of tweets related to HPV vaccines and the social connections between users. Between October 2013 and April 2014, we collected 83,551 English-language tweets that included terms related to HPV vaccines and the 957,865 social connections among 30,621 users posting or reposting the tweets. Tweets were classified as expressing negative or neutral/positive opinions using a machine learning classifier previously trained on a manually labeled sample.

Results

During the 6-month period, 25.13% (20,994/83,551) of tweets were classified as negative; among the 30,621 users that tweeted about HPV vaccines, 9046 (29.54%) were exposed to a majority of negative tweets. The likelihood of a user posting a negative tweet after exposure to a majority of negative opinions was 37.78% (2780/7361) compared to 10.92% (1234/11,296) for users who were exposed to a majority of positive and neutral tweets corresponding to a relative risk of 3.46 (95% CI 3.25-3.67, P<.001).

Conclusions

The heterogeneous community structure on Twitter appears to skew the information to which users are exposed in relation to HPV vaccines. We found that among users that tweeted about HPV vaccines, those who were more often exposed to negative opinions were more likely to subsequently post negative opinions. Although this research may be useful for identifying individuals and groups currently at risk of disproportionate exposure to misinformation about HPV vaccines, there is a clear need for studies capable of determining the factors that affect the formation and adoption of beliefs about public health interventions.     

Increased Use of Twitter at a Medical Conference: A Report and a Review of the Educational Opportunities

Background

Most consider Twitter as a tool purely for social networking. However, it has been used extensively as a tool for online discussion at nonmedical and medical conferences, and the academic benefits of this tool have been reported. Most anesthetists still have yet to adopt this new educational tool. There is only one previously published report of the use of Twitter by anesthetists at an anesthetic conference. This paper extends that work.

Objective

We report the uptake and growth in the use of Twitter, a microblogging tool, at an anesthetic conference and review the potential use of Twitter as an educational tool for anesthetists.

Methods

A unique Twitter hashtag (#WSM12) was created and promoted by the organizers of the Winter Scientific Meeting held by The Association of Anaesthetists of Great Britain and Ireland (AAGBI) in London in January 2012. Twitter activity was compared with Twitter activity previously reported for the AAGBI Annual Conference (September 2011 in Edinburgh). All tweets posted were categorized according to the person making the tweet and the purpose for which they were being used. The categories were determined from a literature review.

Results

A total of 227 tweets were posted under the #WSM12 hashtag representing a 530% increase over the previously reported anesthetic conference. Sixteen people joined the Twitter stream by using this hashtag (300% increase). Excellent agreement (κ = 0.924) was seen in the classification of tweets across the 11 categories. Delegates primarily tweeted to create and disseminate notes and learning points (55%), describe which session was attended, undertake discussions, encourage speakers, and for social reasons. In addition, the conference organizers, trade exhibitors, speakers, and anesthetists who did not attend the conference all contributed to the Twitter stream. The combined total number of followers of those who actively tweeted represented a potential audience of 3603 people.

Conclusions

This report demonstrates an increase in uptake and growth in the use of Twitter at an anesthetic conference and the review illustrates the opportunities and benefits for medical education in the future.     

Insomnia with Objective Short Sleep Duration is Associated with Deficits in Neuropsychological Performance: A General Population Study

Study Objectives:

To examine the joint effect of insomnia and objective short sleep duration on neuropsychological performance.

Design:

Representative cross-sectional study.

Setting:

Sleep laboratory.

Participants:

1,741 men and women randomly selected from central Pennsylvania.

Interventions:

None.

Measurements:

Insomnia (n = 116) was defined by a complaint of insomnia with a duration ≥ 1 year and the absence of sleep disordered breathing (SDB), while normal sleep (n = 562) was defined as the absence of insomnia, excessive daytime sleepiness, and SDB. Both groups were split according to polysomnographic sleep duration into 2 categories: ≥ 6 h of sleep (“normal sleep duration”) and < 6 h of sleep (“short sleep duration”). We compared the groups'' performance on a comprehensive neuropsychological battery that measured processing speed, attention, visual memory, and verbal fluency, while controlling for age, race, gender, education, body mass index, and physical and mental health.

Results:

No significant differences were detected between insomniacs and controls. However, the insomnia with short sleep duration group compared to the control with normal or short sleep duration groups showed poorer neuropsychological performance in variables such as processing speed, set-switching attention, and number of visual memory errors and omissions. In contrast, the insomnia with normal sleep duration group showed no significant deficits.

Conclusions:

Insomnia with objective short sleep duration is associated with deficits in set-switching attentional abilities, a key component of the “executive control of attention.” These findings suggest that objective sleep duration may predict the severity of chronic insomnia, including its effect on neurocognitive function.

Citation:

Fernandez-Mendoza J; Calhoun S; Bixler EO; Pejovic S; Karataraki M; Liao D; Vela-Bueno A; Ramos-Platon MJ; Sauder KA; Vgontzas AN. Insomnia with objective short sleep duration is associated with deficits in neuropsychological performance: a general population study. SLEEP 2010;33(4):459-465.     

Mapping Physician Twitter Networks: Describing How They Work as a First Step in Understanding Connectivity,Information Flow,and Message Diffusion

Background

Twitter is becoming an important tool in medicine, but there is little information on Twitter metrics. In order to recommend best practices for information dissemination and diffusion, it is important to first study and analyze the networks.

Objective

This study describes the characteristics of four medical networks, analyzes their theoretical dissemination potential, their actual dissemination, and the propagation and distribution of tweets.

Methods

Open Twitter data was used to characterize four networks: the American Medical Association (AMA), the American Academy of Family Physicians (AAFP), the American Academy of Pediatrics (AAP), and the American College of Physicians (ACP). Data were collected between July 2012 and September 2012. Visualization was used to understand the follower overlap between the groups. Actual flow of the tweets for each group was assessed. Tweets were examined using Topsy, a Twitter data aggregator.

Results

The theoretical information dissemination potential for the groups is large. A collective community is emerging, where large percentages of individuals are following more than one of the groups. The overlap across groups is small, indicating a limited amount of community cohesion and cross-fertilization. The AMA followers’ network is not as active as the other networks. The AMA posted the largest number of tweets while the AAP posted the fewest. The number of retweets for each organization was low indicating dissemination that is far below its potential.

Conclusions

To increase the dissemination potential, medical groups should develop a more cohesive community of shared followers. Tweet content must be engaging to provide a hook for retweeting and reaching potential audience. Next steps call for content analysis, assessment of the behavior and actions of the messengers and the recipients, and a larger-scale study that considers other medical groups using Twitter.     

The Painful Tweet: Text,Sentiment, and Community Structure Analyses of Tweets Pertaining to Pain

Background

Despite the widespread popularity of social media, little is known about the extent or context of pain-related posts by users of those media.

Objective

The aim was to examine the type, context, and dissemination of pain-related tweets.

Methods

We used content analysis of pain-related tweets from 50 cities to unobtrusively explore the meanings and patterns of communications about pain. Content was examined by location and time of day, as well as within the context of online social networks.

Results

The most common terms published in conjunction with the term “pain” included feel (n=1504), don’t (n=702), and love (n=649). The proportion of tweets with positive sentiment ranged from 13% in Manila to 56% in Los Angeles, CA, with a median of 29% across cities. Temporally, the proportion of tweets with positive sentiment ranged from 24% at 1600 to 38% at 2400, with a median of 32%. The Twitter-based social networks pertaining to pain exhibited greater sparsity and lower connectedness than did those social networks pertaining to common terms such as apple, Manchester United, and Obama. The number of word clusters in proportion to node count was greater for emotion terms such as tired (0.45), happy (0.43), and sad (0.4) when compared with objective terms such as apple (0.26), Manchester United (0.14), and Obama (0.25).

Conclusions

Taken together, our results suggest that pain-related tweets carry special characteristics reflecting unique content and their communication among tweeters. Further work will explore how geopolitical events and seasonal changes affect tweeters’ perceptions of pain and how such perceptions may affect therapies for pain.     

Banking Sleep: Realization of Benefits During Subsequent Sleep Restriction and Recovery

Objective:

Determine whether sleep extension (a) improves alertness and performance during subsequent sleep restriction and (b) impacts the rate at which alertness and performance are restored by post-restriction recovery sleep.

Design:

Participants were randomly assigned to an Extended (10 h time in bed [TIB]) or Habitual TIB [mean (SD) hours = 7.09 (0.7)] sleep group for one week, followed by 1 Baseline (10 hours or habitual TIB), 7 Sleep Restriction (3 h TIB), and 5 Recovery Sleep nights (8 h TIB). Performance and alertness tests were administered hourly between 08:00–18:00 during all in-laboratory phases of the study.

Setting:

Residential sleep/performance testing facility.

Participants:

Twenty-four healthy adults (ages 18–39) participated in the study.

Interventions:

Extended vs. habitual sleep durations prior to sleep restriction.

Results:

Psychomotor vigilance task (PVT) lapses were more frequent and modified maintenance of wakefulness (MWT) sleep latency was shorter in the Habitual group than in the Extended group across the sleep restriction phase. During the Recovery phase, PVT speed rebounded faster (and PVT lapsing recovered significantly after the first night of recovery sleep) in the Extended group. No group differences in subjective sleepiness were evident during any phase of the study.

Conclusion:

The extent to which sleep restriction impairs objectively measured alertness and performance, and the rate at which these impairments are subsequently reversed by recovery sleep, varies as a function of the amount of nightly sleep obtained prior to the sleep restriction period. This suggests that the physiological mechanism(s) underlying chronic sleep debt undergo long-term (days/weeks) accommodative/adaptive changes.

Citation:

Rupp TL; Wesensten NJ; Bliese PD; Balkin TJ. Banking sleep: realization of benefits during subsequent sleep restriction and recovery. SLEEP 2009;32(3):311–321.     

Sleep Duration and Health-Related Quality of Life among Older Adults: A Population-Based Cohort in Spain

Study Objectives:

The few studies that have addressed the association between sleep duration and health-related quality of life (HRQL) were cross-sectional and small-sized, targeted young and middle-aged persons, and did not adjust for the main confounders. This study sought to examine the cross-sectional and longitudinal relationship between habitual sleep duration and HRQL in older adults.

Design:

Prospective study conducted from 2001 through 2003. Sleep duration was self-reported in 2001, and HRQL was measured using the SF-36 questionnaire in 2001 and 2003. Analyses were adjusted for the main confounders.

Setting:

Community-based study.

Participants:

A cohort of 3834 persons representative of the non-institutionalized Spanish population aged 60 years and over.

Intervention:

None.

Measurement and Results:

In comparison with women who slept 7 hours, those with extreme sleep durations (≤ 5 or ≥ 10 h) reported worse scores on the SF-36 physical and mental scales in 2001. Among men, sleeping ≤ 5 h was associated with a worse score in the role-physical scale in 2001. The magnitude of most of these associations was comparable with the reduction in HRQL associated with aging 10 years. Sleep duration in 2001 failed to predict changes in HRQL between 2001 and 2003.

Conclusion:

Extreme sleep durations are a marker of worse HRQL in the elderly.

Citation:

Faubel R; Lopez-Garcia E; Guallar-Castillón P; Balboa-Castillo T; Gutiérrez-Fisac JL; Banegas JR; Rodríguez-Artalejo F. Sleep duration and health-related quality of life among older adults: a population-based cohort in spain. SLEEP 2009;32(8):1059-1068.