Luis Fernandez-Luque

Features of Mobile Diabetes Applications: Review of the Literature and Analysis of Current Applications Compared Against Evidence-Based Guidelines

Background Interest in mobile health (mHealth) applications for self-management of diabetes is growing. In July 2009, we found 60 diabetes applications on iTunes for iPhone; by February 2011 the number had increased by more than 400% to 260. Other mobile platforms reflect a similar trend. Despite the growth, research on both the design and the use of diabetes mHealth applications is scarce. Furthermore, the potential influence of social media on diabetes mHealth applications is largely unexplored. Objective Our objective was to study the salient features of mobile applications for diabetes care, in contrast to clinical guideline recommendations for diabetes self-management. These clinical guidelines are published by health authorities or associations such as the National Institute for Health and Clinical Excellence in the United Kingdom and the American Diabetes Association. Methods We searched online vendor markets (online stores for Apple iPhone, Google Android, BlackBerry, and Nokia Symbian), journal databases, and gray literature related to diabetes mobile applications. We included applications that featured a component for self-monitoring of blood glucose and excluded applications without English-language user interfaces, as well as those intended exclusively for health care professionals. We surveyed the following features: (1) self-monitoring: (1.1) blood glucose, (1.2) weight, (1.3) physical activity, (1.4) diet, (1.5) insulin and medication, and (1.6) blood pressure, (2) education, (3) disease-related alerts and reminders, (4) integration of social media functions, (5) disease-related data export and communication, and (6) synchronization with personal health record (PHR) systems or patient portals. We then contrasted the prevalence of these features with guideline recommendations. Results The search resulted in 973 matches, of which 137 met the selection criteria. The four most prevalent features of the applications available on the online markets (n = 101) were (1) insulin and medication recording, 63 (62%), (2) data export and communication, 61 (60%), (3) diet recording, 47 (47%), and (4) weight management, 43 (43%). From the literature search (n = 26), the most prevalent features were (1) PHR or Web server synchronization, 18 (69%), (2) insulin and medication recording, 17 (65%), (3) diet recording, 17 (65%), and (4) data export and communication, 16 (62%). Interestingly, although clinical guidelines widely refer to the importance of education, this is missing from the top functionalities in both cases. Conclusions While a wide selection of mobile applications seems to be available for people with diabetes, this study shows there are obvious gaps between the evidence-based recommendations and the functionality used in study interventions or found in online markets. Current results confirm personalized education as an underrepresented feature in diabetes mobile applications. We found no studies evaluating social media concepts in diabetes self-management on mobile devices, and its potential remains largely unexplored.

Misleading Health-Related Information Promoted Through Video-Based Social Media: Anorexia on YouTube

Introduction The amount of information being uploaded onto social video platforms, such as YouTube, Vimeo, and Veoh, continues to spiral, making it increasingly difficult to discern reliable health information from misleading content. There are thousands of YouTube videos promoting misleading information about anorexia (eg, anorexia as a healthy lifestyle). Objective The aim of this study was to investigate anorexia-related misinformation disseminated through YouTube videos. Methods We retrieved YouTube videos related to anorexia using the keywords anorexia, anorexia nervosa, proana, and thinspo on October 10, 2011.Three doctors reviewed 140 videos with approximately 11 hours of video content, classifying them as informative, pro-anorexia, or others. By informative we mean content describing the health consequences of anorexia and advice on how to recover from it; by pro-anorexia we mean videos promoting anorexia as a fashion, a source of beauty, and that share tips and methods for becoming and remaining anorexic. The 40 most-viewed videos (20 informative and 20 pro-anorexia videos) were assessed to gauge viewer behavior. Results The interrater agreement of classification was moderate (Fleiss’ kappa=0.5), with 29.3% (n=41) being rated as pro-anorexia, 55.7% (n=78) as informative, and 15.0% (n=21) as others. Pro-anorexia videos were favored 3 times more than informative videos (odds ratio [OR] 3.3, 95% CI 3.3-3.4, P<.001). Conclusions Pro-anorexia information was identified in 29.3% of anorexia-related videos. Pro-anorexia videos are less common than informative videos; however, in proportional terms, pro-anorexia content is more highly favored and rated by its viewers. Efforts should focus on raising awareness, particularly among teenagers, about the trustworthiness of online information about beauty and healthy lifestyles. Health authorities producing videos to combat anorexia should consider involving celebrities and models to reach a wider audience. More research is needed to study the characteristics of pro-anorexia videos in order to develop algorithms that will automatically detect and filter those videos before they become popular.

Review of Extracting Information From the Social Web for Health Personalization

In recent years the Web has come into its own as a social platform where health consumers are actively creating and consuming Web content. Moreover, as the Web matures, consumers are gaining access to personalized applications adapted to their health needs and interests. The creation of personalized Web applications relies on extracted information about the users and the content to personalize. The Social Web itself provides many sources of information that can be used to extract information for personalization apart from traditional Web forms and questionnaires. This paper provides a review of different approaches for extracting information from the Social Web for health personalization. We reviewed research literature across different fields addressing the disclosure of health information in the Social Web, techniques to extract that information, and examples of personalized health applications. In addition, the paper includes a discussion of technical and socioethical challenges related to the extraction of information for health personalization.

Identifying Measures Used for Assessing Quality of YouTube Videos with Patient Health Information: A Review of Current Literature

Background Recent publications on YouTube have advocated its potential for patient education. However, a reliable description of what could be considered quality information for patient education on YouTube is missing. Objective To identify topics associated with the concept of quality information for patient education on YouTube in the scientific literature. Methods A literature review was performed in MEDLINE, ISI Web of Knowledge, Scopus, and PsychINFO. Abstract selection was first conducted by two independent reviewers; discrepancies were discussed in a second abstract review with two additional independent reviewers. Full text of selected papers were analyzed looking for concepts, definitions, and topics used by its authors that focused on the quality of information on YouTube for patient education. Results In total, 456 abstracts were extracted and 13 papers meeting eligibility criteria were analyzed. Concepts identified related to quality of information for patient education are categorized as expert-driven, popularity-driven, or heuristic-driven measures. These include (in descending order): (1) quality of content in 10/13 (77%), (2) view count in 9/13 (69%), (3) health professional opinion in 8/13 (62%), (4) adequate length or duration in 6/13 (46%), (5) public ratings in 5/13 (39%), (6) adequate title, tags, and description in 5/13 (39%), (7) good description or a comprehensive narrative in 4/13 (31%), (8) evidence-based practices included in video in 4/13 (31%), (9) suitability as a teaching tool in 4/13 (31%), (10) technical quality in 4/13 (31%), (11) credentials provided in video in 4/13 (31%), (12) enough amount of content to identify its objective in 3/13 (23%), and (13) viewership share in 2/13 (15%). Conclusions Our review confirms that the current topics linked to quality of information for patient education on YouTube are unclear and not standardized. Although expert-driven, popularity-driven, or heuristic-driven measures are used as proxies to estimate the quality of video information, caution should be applied when using YouTube for health promotion and patient educational material.

Pro-Anorexia and Pro-Recovery Photo Sharing: A Tale of Two Warring Tribes

Background There is widespread use of the Internet to promote anorexia as a lifestyle choice. Pro-anorexia content can be harmful for people affected or at risk of having anorexia. That movement is actively engaged in sharing photos on social networks such as Flickr. Objective To study the characteristics of the online communities engaged in disseminating content that encourages eating disorders (known as “pro-anorexia”) and to investigate if the posting of such content is discouraged by the posting of recovery-oriented content. Methods The extraction of pro-anorexia and pro-recovery photographs from the photo sharing site Flickr pertaining to 242,710 photos from 491 users and analyzing four separate social networks therein. Results Pro-anorexia and pro-recovery communities interact to a much higher degree among themselves than what is expected from the distribution of contacts (only 59-72% of contacts but 74-83% of comments are made to members inside the community). Pro-recovery users employ similar words to those used by pro-anorexia users to describe their photographs, possibly in order to ensure that their content appears when pro-anorexia users search for images. Pro-anorexia users who are exposed to comments from the opposite camp are less likely to cease posting pro-anorexia photographs than those who do not receive such comments (46% versus 61%), and if they cease, they do so approximately three months later. Our observations show two highly active communities, where most interaction is within each community. However, the pro-recovery community takes steps to ensure that their content is visible to the pro-anorexia community, both by using textual descriptions of their photographs that are similar to those used by the pro-anorexia group and by commenting to pro-anorexia content. The latter activity is, however, counterproductive, as it entrenches pro-anorexia users in their stance. Conclusions Our results highlight the nature of pro-anorexia and pro-recovery photo sharing and accentuate the need for clinicians to be aware of such content and its effect on their patients. Our findings suggest that some currently used interventions are not useful in helping pro-anorexia users recover. Thus, future work should focus on new intervention methods, possibly tailored to individual characteristics.

Social media in health - what are the safety concerns for health consumers?

Recent literature has discussed the unintended consequences of clinical information technologies (IT) on patient safety, yet there has been little discussion about the safety concerns in the area of consumer health IT. This paper presents a range of safety concerns for consumers in social media, with a case study on YouTube. We conducted a scan of abstracts on ‘quality criteria’ related to YouTube. Five areas regarding the safety of YouTube for consumers were identified: (a) harmful health material targeted at consumers (such as inappropriate marketing of tobacco or direct-to-consumer drug advertising); (b) public display of unhealthy behaviour (such as people displaying self-injury behaviours or hurting others); (c) tainted public health messages (i.e. the rise of negative voices against public health messages); (d) psychological impact from accessing inappropriate, offensive or biased social media content; and (e) using social media to distort policy and research funding agendas. The examples presented should contribute to a better understanding about how to promote a safe consumption and production of social media for consumers, and an evidence-based approach to designing social media interventions for health. The potential harm associated with the use of unsafe social media content on the Internet is a major concern. More empirical and theoretical studies are needed to examine how social media influences consumer health decisions, behaviours and outcomes, and devise ways to deter the dissemination of harmful influences in social media.

Health and Social Media: Perfect Storm of Information

Objectives The use of Internet in the health domain is becoming a major worldwide trend. Millions of citizens are searching online health information and also publishing content about their health. Patients are engaging with other patients in online communities using different types of social media. The boundaries between mobile health, social media, wearable, games, and big data are becoming blurrier due the integration of all those technologies. In this paper we provide an overview of the major research challenges with the area of health social media. Methods We use several study cases to exemplify the current trends and highlight future research challenges. Results Internet is exploding and is being used for health purposes by a great deal of the population. Social networks have a powerful influence in health decisions. Given the lack of knowledge on the use of health social media, there is a need for complex multidisciplinary research to help us understand how to use social networks in favour of public health. A bigger understanding of social media will give health authorities new tools to help decision-making at global, national, local, and corporate level. Conclusions There is an unprecedented amount of data that can be used in public health due the potential combination of data acquired from mobile phones, Electronic Health Records, social media, and other sources. To identify meaningful information from those data sources it is not trial. Moreover, new analytics tools will need to be developed to analyse those sources of data in a way that it can benefit healthcare professionals and authorities.

HealthTrust: A Social Network Approach for Retrieving Online Health Videos

Background Social media are becoming mainstream in the health domain. Despite the large volume of accurate and trustworthy health information available on social media platforms, finding good-quality health information can be difficult. Misleading health information can often be popular (eg, antivaccination videos) and therefore highly rated by general search engines. We believe that community wisdom about the quality of health information can be harnessed to help create tools for retrieving good-quality social media content. Objectives To explore approaches for extracting metrics about authoritativeness in online health communities and how these metrics positively correlate with the quality of the content. Methods We designed a metric, called HealthTrust, that estimates the trustworthiness of social media content (eg, blog posts or videos) in a health community. The HealthTrust metric calculates reputation in an online health community based on link analysis. We used the metric to retrieve YouTube videos and channels about diabetes. In two different experiments, health consumers provided 427 ratings of 17 videos and professionals gave 162 ratings of 23 videos. In addition, two professionals reviewed 30 diabetes channels. Results HealthTrust may be used for retrieving online videos on diabetes, since it performed better than YouTube Search in most cases. Overall, of 20 potential channels, HealthTrust’s filtering allowed only 3 bad channels (15%) versus 8 (40%) on the YouTube list. Misleading and graphic videos (eg, featuring amputations) were more commonly found by YouTube Search than by searches based on HealthTrust. However, some videos from trusted sources had low HealthTrust scores, mostly from general health content providers, and therefore not highly connected in the diabetes community. When comparing video ratings from our reviewers, we found that HealthTrust achieved a positive and statistically significant correlation with professionals (Pearson r 10 = .65, P = .02) and a trend toward significance with health consumers (r 7 = .65, P = .06) with videos on hemoglobinA1 c, but it did not perform as well with diabetic foot videos. Conclusions The trust-based metric HealthTrust showed promising results when used to retrieve diabetes content from YouTube. Our research indicates that social network analysis may be used to identify trustworthy social media in health communities.

The role of taxonomies in social media and the semantic web for health education. A study of SNOMED CT terms in YouTube health video tags.

BACKGROUND An increasing amount of health education resources for patients and professionals are distributed via social media channels. For example, thousands of health education videos are disseminated via YouTube. Often, tags are assigned by the disseminator. However, the lack of use of standardized terminologies in those tags and the presence of misleading videos make it particularly hard to retrieve relevant videos. OBJECTIVES i) Identify the use of standardized medical thesauri (SNOMED CT) in YouTube Health videos tags from preselected YouTube Channels and demonstrate an information technology (IT) architecture for treating the tags of these health (video) resources. ii) Investigate the relative percentage of the tags used that relate to SNOMED CT terms. As such resources may play a key role in educating professionals and patients, the use of standardized vocabularies may facilitate the sharing of such resources. iii) Demonstrate how such resources may be properly exploited within the new generation of semantically enriched content or learning management systems that allow for knowledge expansion through the use of linked medical data and numerous literature resources also described through the same vocabularies. METHODS We implemented a video portal integrating videos from 500 US Hospital channels. The portal integrated 4,307 YouTube videos regarding surgery as described by 64,367 tags. BioPortal REST services were used within our portal to match SNOMED CT terms with YouTube tags by both exact match and non-exact match. The whole architecture was complemented with a mechanism to enrich the retrieved video resources with other educational material residing in other repositories by following contemporary semantic web advances, in the form of Linked Open Data (LOD) principles. RESULTS The average percentage of YouTube tags that were expressed using SNOMED CT terms was about 22.5%, while one third of YouTube tags per video contained a SNOMED CT term in a loose search; this analogy became one tenth in the case of exact match. Retrieved videos were then linked further to other resources by using LOD compliant systems. Such results were exemplified in the case of systems and technologies used in the mEducator EC funded project. CONCLUSION YouTube Health videos can be searched for and retrieved using SNOMED CT terms with a high possibility of identifying health videos that users want based on their search criteria. Despite the fact that tagging of this information with SNOMED CT terms may vary, its availability and linked data capacity opens the door to new studies for personalized retrieval of content and linking with other knowledge through linked medical data and semantic advances in (learning) content management systems.

The role of social media for patients and consumer health : contribution of the IMIA consumer health informatics working group

OBJECTIVES : To provide an overview on social media for consumers and patients in areas of health behaviours and outcomes. METHODS A directed review of recent literature. RESULTS : We discuss the limitations and challenges of social media, ranging from social network sites (SNSs), computer games, mobile applications, to online videos. An overview of current users of social media (Generation Y), and potential users (such as low socioeconomic status and the chronically ill populations) is also presented. Future directions in social media research are also discussed. CONCLUSIONS : We encourage the health informatics community to consider the socioeconomic class, age, culture, and literacy level of their populations, and select an appropriate medium and platform when designing social networked interventions for health. Little is known about the impact of second-hand experiences faciliated by social media, nor the quality and safety of social networks on health. Methodologies and theories from human computer interaction, human factors engineering and psychology may help guide the challenges in designing and evaluating social networked interventions for health. Further, by analysing how people search and navigate social media for health purposes, infodemiology and infoveillance are promising areas of research that should provide valuable insights on present and emergening health behaviours on a population scale.