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Background: Survey response rates have been declining over the past decade. The more widespread use of the Internet and Web-based technologies among potential health survey participants suggests that Web-based questionnaires may be an alternative to paper questionnaires in future epidemiological studies. Objective: To compare response rates in a population of parents by using 4 different modes of data collection for a questionnaire survey of which 1 involved a nonmonetary incentive. Methods: A random sample of 3148 parents of Danish children aged 2-17 years were invited to participate in the Danish part of the NordChild 2011 survey on their children’s health and welfare. NordChild was conducted in 1984 and 1996 in collaboration with Finland, Iceland, Norway, and Sweden using mailed paper questionnaires only. In 2011, all countries used conventional paper versions only except Denmark where the parents were randomized into 4 groups: (1) 789 received a paper questionnaire only (paper), (2) 786 received the paper questionnaire and a log-in code to the Web-based questionnaire (paper/Web), (3) 787 received a log-in code to the Web-based questionnaire (Web), and (4) 786 received log-in details to the Web-based questionnaire and were given an incentive consisting of a chance to win a tablet computer (Web/tablet). In connection with the first reminder, the nonresponders in the paper, paper/Web, and Web groups were also present with the opportunity to win a tablet computer as a means of motivation. Descriptive analysis was performed using chi-square tests. Odds ratios were used to estimate differences in response rates between the 4 modes. Results: In 2011, 1704 of 3148 (54.13%) respondents answered the Danish questionnaire. The highest response rate was with the paper mode (n=443, 56.2%). The other groups had similar response rates: paper/Web (n=422, 53.7%), Web (n=420, 53.4%), and Web/tablet (n=419, 53.3%) modes. Compared to the paper mode, the odds for response rate in the paper/Web decreased by 9% (OR 0.91, 95% CI 0.74-1.10) and by 11% (OR 0.89, 95% CI 0.73-1.09) in the Web and Web/tablet modes. The total number of responders for NordChild declined from 10,291 of 15,339 (67.09%) in 1984 and 10,667 of 15,254 (69.93%) in 1996 to 7805 of 15,945 (48.95%) in 2011 with similar declines in all 5 Nordic countries. Conclusions: Web-based questionnaires could replace traditional paper questionnaires with minor effects on response rates and lower costs. The increasing effect on the response rate on participants replying for a nonmonetary incentive could only be estimated within the 2 Web-based questionnaire modes before the first reminder. Alternative platforms to reach higher participation rates in population surveys should reflect the development of electronic devices and the ways in which the population primarily accesses the Internet. [J Med Internet Res 2013;15(8):e173]

Background As the use of smartphones and mobile apps is increasing, mobile health (mHealth) can be used as a cost-effective option to provide behavioral interventions aimed at educating and promoting self-management for chronic diseases such as diabetes. Although many mobile software apps have been developed for this purpose, they usually lack a theoretical foundation and do not follow the guidelines suggested for evidence-based practice. Therefore, this study aimed to develop a theory-based self-management app for people with type 2 diabetes and provide an app based on a needs assessment analysis. Objective This paper describes the development and usability evaluation of a cloud-based and mobile-based diabetes self-management app designed to help people with diabetes change their health behavior and also enable remote monitoring by health care providers. Methods The development of this mHealth solution comprises 3 phases. Phase I: feature extraction of the Android apps that had a user rating of 4 stars or more and review of papers related to mHealth for diabetes self-management were performed followed by seeking expert opinions about the extracted features to determine the essential features of the app. Phase II: design and implementation included selecting which behavioral change and structural theories were to be applied the app and design of the website. Phase III: evaluation of the usability and user experience of the mobile app by people with diabetes and the portal by health care providers using the User Experience Questionnaire. Results The developed mobile app includes modules that support several features. A person’s data were entered or collected and viewed in the form of graphs and tables. The theoretical foundation of behavioral intervention is the transtheoretical model. Users were able to receive customized messages based on the behavioral change preparation stage using the Kreuter algorithm. The clinician’s portal was used by health care providers to monitor the patients. The results of the usability evaluation revealed overall user satisfaction with the app. Conclusions Mobile- and cloud-based systems may be an effective tool for facilitating the modification of self-management of chronic care. The results of this study showed that the usability of mobile- and cloud-based systems can be satisfactory and promising. Given that the study used a behavioral model, assessment of the effectiveness of behavior change over time requires further research with long-term follow-up.

Background Crowdsourcing services, such as Amazon Mechanical Turk (AMT), allow researchers to use the collective intelligence of a wide range of web users for labor-intensive tasks. As the manual verification of the quality of the collected results is difficult because of the large volume of data and the quick turnaround time of the process, many questions remain to be explored regarding the reliability of these resources for developing digital public health systems. Objective This study aims to explore and evaluate the application of crowdsourcing, generally, and AMT, specifically, for developing digital public health surveillance systems. Methods We collected 296,166 crowd-generated labels for 98,722 tweets, labeled by 610 AMT workers, to develop machine learning (ML) models for detecting behaviors related to physical activity, sedentary behavior, and sleep quality among Twitter users. To infer the ground truth labels and explore the quality of these labels, we studied 4 statistical consensus methods that are agnostic of task features and only focus on worker labeling behavior. Moreover, to model the meta-information associated with each labeling task and leverage the potential of context-sensitive data in the truth inference process, we developed 7 ML models, including traditional classifiers (offline and active), a deep learning–based classification model, and a hybrid convolutional neural network model. Results Although most crowdsourcing-based studies in public health have often equated majority vote with quality, the results of our study using a truth set of 9000 manually labeled tweets showed that consensus-based inference models mask underlying uncertainty in data and overlook the importance of task meta-information. Our evaluations across 3 physical activity, sedentary behavior, and sleep quality data sets showed that truth inference is a context-sensitive process, and none of the methods studied in this paper were consistently superior to others in predicting the truth label. We also found that the performance of the ML models trained on crowd-labeled data was sensitive to the quality of these labels, and poor-quality labels led to incorrect assessment of these models. Finally, we have provided a set of practical recommendations to improve the quality and reliability of crowdsourced data. Conclusions Our findings indicate the importance of the quality of crowd-generated labels in developing ML models designed for decision-making purposes, such as public health surveillance decisions. A combination of inference models outlined and analyzed in this study could be used to quantitatively measure and improve the quality of crowd-generated labels for training ML models.

Background Online health communities (OHCs) have increasingly gained traction with patients, caregivers, and supporters globally. Chinese OHCs are no exception. However, user-generated content (UGC) and the associated user behaviors in Chinese OHCs are largely underexplored and rarely analyzed systematically, forfeiting valuable opportunities for optimizing treatment design and care delivery with insights gained from OHCs. Objective This study aimed to reveal both the shared and distinct characteristics of 2 popular OHCs in China by systematically and comprehensively analyzing their UGC and the associated user behaviors. Methods We concentrated on studying the lung cancer forum (LCF) and breast cancer forum (BCF) on Mijian, and the diabetes consultation forum (DCF) on Sweet Home, because of the importance of the 3 diseases among Chinese patients and their prevalence on Chinese OHCs in general. Our analysis explored the key user activities, small-world effect, and scale-free characteristics of each social network. We examined the UGC of these forums comprehensively and adopted the weighted knowledge network technique to discover salient topics and latent relations among these topics on each forum. Finally, we discussed the public health implications of our analysis findings. Results Our analysis showed that the number of reads per thread on each forum followed gamma distribution (HL=0, HB=0, and HD=0); the number of replies on each forum followed exponential distribution (adjusted RL2=0.946, adjusted RB2=0.958, and adjusted RD2=0.971); and the number of threads a user is involved with (adjusted RL2=0.978, adjusted RB2=0.964, and adjusted RD2=0.970), the number of followers of a user (adjusted RL2=0.989, adjusted RB2=0.962, and adjusted RD2=0.990), and a user’s degrees (adjusted RL2=0.997, adjusted RB2=0.994, and adjusted RD2=0.968) all followed power-law distribution. The study further revealed that users are generally more active during weekdays, as commonly witnessed in all 3 forums. In particular, the LCF and DCF exhibited high temporal similarity (ρ=0.927; P Conclusions UGC and related online user behaviors in Chinese OHCs can be leveraged as important sources of information to gain insights regarding individual and population health conditions. Effective and timely mining and utilization of such content can continuously provide valuable firsthand clues for enhancing the situational awareness of health providers and policymakers.

Background eHealth literacy (EHL) refers to a variety of capabilities that enable individuals to obtain health information from electronic resources and apply it to solve health problems. With the digitization of health care and the wide availability of health apps, a more diverse range of eHealth skills is required to properly use such health facilities. Existing EHL measurements focus mainly on the skill of obtaining health information (Web 1.0), whereas skills for web-based interactions (Web 2.0) and self-managing health data and applying information (Web 3.0) have not been well measured. Objective This study aims to develop an EHL scale (eHLS) termed eHLS-Web3.0 comprising a comprehensive spectrum of Web 1.0, 2.0, and 3.0 skills to measure EHL, and evaluate its validity and reliability along with the measurement invariance among college students. Methods In study 1, 421 Chinese college students (mean age 20.5, SD 1.4 years; 51.8% female) and 8 health experts (mean age 38.3, SD 5.9 years; 87.5% female) were involved to develop the eHLS-Web3.0. The scale development included three steps: item pool generation, content validation, and exploratory factor analysis. In study 2, 741 college students (mean age 21.3, SD 1.4 years; 52.2% female) were recruited from 4 Chinese cities to validate the newly developed eHLS-Web3.0. The construct validity, convergent validity, concurrent validity, internal consistency reliability, test-retest reliability, and measurement invariance across genders, majors, and regions were examined by a series of statistical analyses, including confirmatory factor analysis (CFA) and multigroup CFAs using SPSS and Mplus software packages. Results Based on the item pool of 374 statements collected during the conceptual development, 24 items (4-10 items per subscale) were generated and adjusted after cognitive testing and content validity examination. Through exploratory factor analysis, a 3-factor eHLS-Web3.0 was finally developed, and it included acquisition (8 items), verification (6 items), and application (10 items). In study 2, CFAs supported the construct validity of the 24-item 3D eHLS-Web3.0 (χ2244=903.076, χ2244=3.701, comparative fit index=0.924, Tucker-Lewis index=0.914, root mean square error of approximation [RMSEA]=0.06, and standardized root mean residual [SRMR]=0.051). The average variance extracted (AVE) value of 0.58 and high correlation between eHLS-Web3.0 subscales and the eHealth Literacy Scale (r=0.725-0.880, P Conclusions The 24-item 3D eHLS-Web3.0 proved to be a reliable and valid measurement tool for EHL in the Web 3.0 context among Chinese college students.

Background It is now common to search for health information online. A 2013 Pew Research Center survey found that 77% of online health seekers began their query at a search engine. The widespread use of online health information seeking also applies to women’s reproductive health. Despite online interest in birth control, not much is known about related interests and concerns reflected in the search terms in the United States. Objective In this study, we identify the top search terms on Google related to birth control in Louisiana and Mississippi and compare those results to the broader United States, examining how Google searches on birth control have evolved over time and identifying regional variation within states. Methods We accessed search data on birth control from 2014-2018 from 2 Google application programming interfaces (APIs), Google Trends and Google Health Trends. We selected Google as it is the most commonly used search engine. We focused our analysis on data from 2017 and compared with 2018 data as appropriate. To assess trends, we analyzed data from 2014 through 2018. To compare the relative search frequencies of the top queries across Louisiana, Mississippi, and the United States, we used the Google Health Trends API. Relative search volume by designated marketing area (DMA) gave us the rankings of search volume for each birth control method in each DMA as compared to one another. Results Results showed that when people searched for “birth control” in Louisiana and the broader United States, they were searching for information on a diverse spectrum of methods. This differs from Mississippi, where the data indicated people were mainly searching for information related to birth control pills. Across all locations, searches for birth control pills were significantly higher than any other queries related to birth control in the United States, Louisiana, and Mississippi, and this trend remained constant from 2014 to 2018. Regional level analysis showed variations in search traffic for birth control across each state. Conclusions The internet is a growing source of health information for many users, including information on birth control. Understanding popular Google search queries on birth control can inform in-person discussions initiated by family planning practitioners and broader birth control messaging campaigns. International Registered Report Identifier (IRRID) RR2-10.2196/16543

Background The popularity of mobile health (mHealth) technology has resulted in the development of numerous apps for almost every condition and disease management. mHealth and eHealth solutions for increasing awareness about, and safety around, intimate partner violence are no exception. These apps allow women to control access to these resources and provide unlimited, and with the right design features, safe access when these resources are needed. Few apps, however, have been designed in close collaboration with intended users to ensure relevance and effectiveness. Objective The objective of this paper is to discuss the design of a suite of evidence-based mHealth and eHealth apps to facilitate early identification of unsafe relationship behaviors and tailored safety planning to reduce harm from violence including the methods by which we collaborated with and sought input from a population of intended users. Methods A user-centered approach with aspects of human-centered design was followed to design a suite of 3 app-based safety planning interventions. Results This review of the design suite of app-based interventions revealed challenges faced and lessons learned that may inform future efforts to design evidence-based mHealth and eHealth interventions. Conclusions Following a user-centered approach can be helpful in designing mHealth and eHealth interventions for marginalized and vulnerable populations, and led to novel insights that improved the design of our interventions.

Background Many diseases occurring in adults can be pinned down to early childhood and birth cohorts are the optimal means to study this connection. Birth cohorts have contributed to the understanding of many diseases and their risk factors. Objective To improve the knowledge of the health status of Italian children early on and how it is affected by social and health determinants, we set up a longitudinal, prospective, national-level, population-based birth cohort, the NASCITA study (NAscere e creSCere in ITAlia). The main aim of this cohort is to evaluate physical, cognitive, and psychological development; health status; and health resource use in the first 6 years of life in newborns, and potential associated factors. A web-based system was set up with the aim to host the cohort; provide ongoing information to pediatricians and to families; and facilitate accurate data input, monitoring, and analysis. This article describes the informatics methodology used to set up and maintain the NASCITA cohort with its web-based platform, and provides a general description of the data on children aged over 7 months. Methods Family pediatricians were contacted for participation in the cohort and enrolled newborns from April 2019 to July 2020 at their first well-child visit. Information collected included basic data that are part of those routinely collected by the family pediatricians, but also parental data, such as medical history, characteristics and lifestyle, and indoor and outdoor environment. A specific web portal for the NASCITA cohort study was developed and an electronic case report form for data input was created and tested. Interactive data charts, including growth curves, are being made available to pediatricians with their patients’ data. Newsletters covering the current biomedical literature on child cohorts are periodically being put up for pediatricians, and, for parents, evidence-based information on common illnesses and problems in children. Results The entire cohort population consists of 5166 children, with 139 participating pediatricians, distributed throughout Italy. The number of children enrolled per pediatrician ranged from 1 to 100. The 5166 enrolled children represent 66.55% (5166/7763) of the children born in all of 2018 covered by the same pediatricians participating in the cohort. The number of children aged over 7 months at the time of these analyses, and for whom the most complete data were available upon initial analyses, was 4386 (2226/4381 males [50.81%] and 142/4370 twins [3.25%]). The age of the mothers at birth of the 4386 children ranged from 16 to 54 years. Most newborns’ mothers (3758/4367, 86.05%) were born in Italy, followed by mothers born in Romania (101/4367, 2.31%), Albania (75/4367, 1.72%), and Morocco (60/4367, 1.37%). Concerning the newborns, 138/4386 (3.15%) were born with malformations and 352/4386 (8.03%) had a disease, most commonly neonatal respiratory distress syndrome (n=52), neonatal jaundice (n=46), and neonatal hypoglycemia (n=45). Conclusions The NASCITA cohort is well underway and the population size will permit significant conclusions to be drawn. The key role of pediatricians in obtaining clinical data directly, along with the national-level representativity, will make the findings even more solid. In addition to promoting accurate data input, the multiple functions of the web portal, with its interactive platform, help maintain a solid relationship with the pediatricians and keep parents informed and interested in participating. Trial Registration ClinicalTrials.gov NCT03894566; https://clinicaltrials.gov/ct2/show/NCT03894566

Background Despite the introduction of the Web Content Accessibility Guidelines and legislations, many websites remain poorly accessible to users with disability, especially those with visual impairment, as the internet has become a more visually complex environment. With increasing reliance on the internet and almost 2 million people in the United Kingdom being affected by vision loss, it is important that they are not overlooked when developing web-based materials. A significant proportion of those affected have irreversible vision loss due to rare genetic eye disorders, and many of them use the internet as a primary source of information for their conditions. However, access to high-quality web-based health information with an inclusive design remains a challenge for many. We have developed a new web-based resource for genetic eye disorders called Gene.Vision that aims to provide a holistic guide for patients, relatives, and health care professionals. Objective Through a usability testing session of our website prototype, this study aims to identify key web-based accessibility features for internet users with vision impairment and to explore whether the contents provided in Gene.Vision are relevant and comprehensible. Methods A face-to-face testing session with 8 participants (5 patients, 2 family members, and 1 member of the public) and 8 facilitators was conducted on a prototype website. Remote testing was performed with another patient due to COVID-19 restrictions. Home page design, navigation, content layout and quality, language, and readability were explored through direct observation and task completion using the think-aloud method. A patient focus group was organized to elicit further feedback. Qualitative data were gathered and analyzed to identify core themes through open and axial coding. Results All participants had good computer literacy; 6 patients with visual impairment used visual aid software including iOS VoiceOver and Speak Screen, iOS Classic Invert, ZoomText 2020, Job Access With Speech, and Nonvisual Desktop Access. The features identified by the participants that will enhance accessibility and usability for users with visual impairment were a consistent website layout, a structured information hierarchy with a clear description of links, good chromatic and luminance contrast, a simple home page with predictable and easy navigation, adaptability to various assistive software, and readable and relevant content. They reported that dynamic content (such as carousels) and large empty spaces reduced accessibility. Information on research, support available, practical advice, and links to charities were incentives for repeated website visits. Conclusions We demonstrated the importance of developing a website with a user-based approach. Through end user testing, we identified several key web-based accessibility features for people with visual impairment. Target end users should always be involved early and throughout the design process to ensure their needs are met. Many of these steps can be implemented easily and will aid in search engine optimization.

Background COVID-19 became a global pandemic not long after its identification in late 2019. The genomes of SARS-CoV-2 are being rapidly sequenced and shared on public repositories. To keep up with these updates, scientists need to frequently refresh and reclean data sets, which is an ad hoc and labor-intensive process. Further, scientists with limited bioinformatics or programming knowledge may find it difficult to analyze SARS-CoV-2 genomes. Objective To address these challenges, we developed CoV-Seq, an integrated web server that enables simple and rapid analysis of SARS-CoV-2 genomes. Methods CoV-Seq is implemented in Python and JavaScript. The web server and source code URLs are provided in this article. Results Given a new sequence, CoV-Seq automatically predicts gene boundaries and identifies genetic variants, which are displayed in an interactive genome visualizer and are downloadable for further analysis. A command-line interface is available for high-throughput processing. In addition, we aggregated all publicly available SARS-CoV-2 sequences from the Global Initiative on Sharing Avian Influenza Data (GISAID), National Center for Biotechnology Information (NCBI), European Nucleotide Archive (ENA), and China National GeneBank (CNGB), and extracted genetic variants from these sequences for download and downstream analysis. The CoV-Seq database is updated weekly. Conclusions We have developed CoV-Seq, an integrated web service for fast and easy analysis of custom SARS-CoV-2 sequences. The web server provides an interactive module for the analysis of custom sequences and a weekly updated database of genetic variants of all publicly accessible SARS-CoV-2 sequences. We believe CoV-Seq will help improve our understanding of the genetic underpinnings of COVID-19.

Background Social media platforms such as YouTube are hotbeds for the spread of misinformation about vaccines. Objective The aim of this study was to explore how individuals are exposed to antivaccine misinformation on YouTube based on whether they start their viewing from a keyword-based search or from antivaccine seed videos. Methods Four networks of videos based on YouTube recommendations were collected in November 2019. Two search networks were created from provaccine and antivaccine keywords to resemble goal-oriented browsing. Two seed networks were constructed from conspiracy and antivaccine expert seed videos to resemble direct navigation. Video contents and network structures were analyzed using the network exposure model. Results Viewers are more likely to encounter antivaccine videos through direct navigation starting from an antivaccine video than through goal-oriented browsing. In the two seed networks, provaccine videos, antivaccine videos, and videos containing health misinformation were all found to be more likely to lead to more antivaccine videos. Conclusions YouTube has boosted the search rankings of provaccine videos to combat the influence of antivaccine information. However, when viewers are directed to antivaccine videos on YouTube from another site, the recommendation algorithm is still likely to expose them to additional antivaccine information.

Background The COVID-19 outbreak was designated a global pandemic on March 11, 2020. The relationship between vaping and contracting COVID-19 is unclear, and information on the internet is conflicting. There is some scientific evidence that vaping cannabidiol (CBD), an active ingredient in cannabis that is obtained from the hemp plant, or other substances is associated with more severe manifestations of COVID-19. However, there is also inaccurate information that vaping can aid COVID-19 treatment, as well as expert opinion that CBD, possibly administered through vaping, can mitigate COVID-19 symptoms. Thus, it is necessary to study the spread of inaccurate information to better understand how to promote scientific knowledge and curb inaccurate information, which is critical to the health of vapers. Inaccurate information about vaping and COVID-19 may affect COVID-19 treatment outcomes. Objective Using structural topic modeling, we aimed to map temporal trends in the web-based vaping narrative (a large data set comprising web-based vaping chatter from several sources) to indicate how the narrative changed from before to during the COVID-19 pandemic. Methods We obtained data using a textual query that scanned a data pool of approximately 200,000 different domains (4,027,172 documents and 361,100,284 words) such as public internet forums, blogs, and social media, from August 1, 2019, to April 21, 2020. We then used structural topic modeling to understand changes in word prevalence and semantic structures within topics around vaping before and after December 31, 2019, when COVID-19 was reported to the World Health Organization. Results Broadly, the web-based vaping narrative can be organized into the following groups or archetypes: harms from vaping; Vaping Regulation; Vaping as Harm Reduction or Treatment; and Vaping Lifestyle. Three archetypes were observed prior to the emergence of COVID-19; however, four archetypes were identified post–COVID-19 (Vaping as Harm Reduction or Treatment was the additional archetype). A topic related to CBD product preference emerged after COVID-19 was first reported, which may be related to the use of CBD by vapers as a COVID-19 treatment. Conclusions Our main finding is the emergence of a vape-administered CBD treatment narrative around COVID-19 when comparing the web-based vaping narratives before and during the COVID-19 pandemic. These results are key to understanding how vapers respond to inaccurate information about COVID-19, optimizing treatment of vapers who contract COVID-19, and possibly minimizing instances of inaccurate information. The findings have implications for the management of COVID-19 among vapers and the monitoring of web-based content pertinent to tobacco to develop targeted interventions to manage COVID-19 among vapers.

Background Clinical governance of medical mobile apps is challenging, and there is currently no standard method for assessing the quality of such apps. In 2018, the National Institute for Health and Care Excellence (NICE) developed a framework for assessing the required level of evidence for digital health technologies (DHTs), as determined by their clinical function. The framework can potentially be used to assess mobile apps, which are a subset of DHTs. To be used reliably in this context, the framework must allow unambiguous classification of an app’s clinical function. Objective The objective of this study was to determine whether mobile health apps could be reliably classified using the NICE evidence standards framework for DHTs. Methods We manually extracted app titles, screenshots, and content descriptions for all apps listed on the National Health Service (NHS) Apps Library website on July 12, 2019; none of the apps were downloaded. Using this information, 2 mobile health (mHealth) researchers independently classified each app to one of the 4 functional tiers (ie, 1, 2, 3a, and 3b) described in the NICE digital technologies evaluation framework. Coders also answered contextual questions from the framework to identify whether apps were deemed to be higher risk. Agreement between coders was assessed using Cohen κ statistic. Results In total, we assessed 76 apps from the NHS Apps Library. There was classification agreement for 42 apps. Of these, 0 apps were unanimously classified into Tier 1; 24, into Tier 2; 15, into Tier 3a; and 3, into Tier 3b. There was disagreement between coders in 34/76 cases (45%); interrater agreement was poor (Cohen κ=0.32, 95% CI 0.16-0.47). Further investigation of disagreements highlighted 5 main explanatory themes: apps that did not correspond to any tier, apps that corresponded to multiple tiers, ambiguous tier descriptions, ambiguous app descriptions, and coder error. Conclusions The current iteration of the NICE evidence standards framework for DHTs did not allow mHealth researchers to consistently and unambiguously classify digital health mobile apps listed on the NHS app library according to their functional tier.

Background Communication campaigns using social media can raise public awareness; however, they are difficult to sustain. A barrier is the need to generate and constantly post novel but on-topic messages, which creates a resource-intensive bottleneck. Objective In this study, we aim to harness the latest advances in artificial intelligence (AI) to build a pilot system that can generate many candidate messages, which could be used for a campaign to suggest novel, on-topic candidate messages. The issue of folic acid, a B-vitamin that helps prevent major birth defects, serves as an example; however, the system can work with other issues that could benefit from higher levels of public awareness. Methods We used the Generative Pretrained Transformer-2 architecture, a machine learning model trained on a large natural language corpus, and fine-tuned it using a data set of autodownloaded tweets about #folicacid. The fine-tuned model was then used as a message engine, that is, to create new messages about this topic. We conducted a web-based study to gauge how human raters evaluate AI-generated tweet messages compared with original, human-crafted messages. Results We found that the Folic Acid Message Engine can easily create several hundreds of new messages that appear natural to humans. Web-based raters evaluated the clarity and quality of a human-curated sample of AI-generated messages as on par with human-generated ones. Overall, these results showed that it is feasible to use such a message engine to suggest messages for web-based campaigns that focus on promoting awareness. Conclusions The message engine can serve as a starting point for more sophisticated AI-guided message creation systems for health communication. Beyond the practical potential of such systems for campaigns in the age of social media, they also hold great scientific potential for the quantitative analysis of message characteristics that promote successful communication. We discuss future developments and obvious ethical challenges that need to be addressed as AI technologies for health persuasion enter the stage.

Background Worldwide, social media traffic increased following the onset of the COVID-19 pandemic. Although the spread of COVID-19 content has been described for several social media platforms (eg, Twitter and Facebook), little is known about how such content is spread via private messaging platforms, such as WhatsApp (WhatsApp LLC). Objective In this study, we documented (1) how WhatsApp is used to transmit COVID-19 content, (2) the characteristics of WhatsApp users based on their usage patterns, and (3) how usage patterns link to COVID-19 concerns. Methods We used the experience sampling method to track day-to-day WhatsApp usage during the COVID-19 pandemic. For 1 week, participants reported each day the extent to which they had received, forwarded, or discussed COVID-19 content. The final data set comprised 924 data points, which were collected from 151 participants. Results During the weeklong monitoring process, most participants (143/151, 94.7%) reported at least 1 COVID-19–related use of WhatsApp. When a taxonomy was generated based on usage patterns, around 1 in 10 participants (21/151, 13.9%) were found to have received and shared a high volume of forwarded COVID-19 content, akin to super-spreaders identified on other social media platforms. Finally, those who engaged with more COVID-19 content in their personal chats were more likely to report having COVID-19–related thoughts throughout the day. Conclusions Our findings provide a rare window into discourse on private messaging platforms. Such data can be used to inform risk communication strategies during the pandemic. Trial Registration ClinicalTrials.gov NCT04367363; https://clinicaltrials.gov/ct2/show/NCT04367363

Background Health care organizations are tasked with providing web-based health resources and information. Usability refers to the ease of user experience on a website. In this study, we conducted a usability analysis of academic medical centers in the United States, which, to the best of our knowledge, has not been previously carried out. Objective The primary aims of the study were to the following: (1) adapt a preexisting usability scoring methodology to academic medical centers; (2) apply and test this methodology on a sample set of academic medical center websites; and (3) make recommendations from these results on potential areas of improvements for our sample of academic medical center websites. Methods All website usability testing took place from June 1, 2020, to December 15, 2020. We replicated a methodology developed in previous literature and applied it to academic medical centers. Our sample included 73 US academic medical centers. Usability was split into four broad categories: accessibility (the ability of those with low levels of computer literacy to access and navigate the hospital’s website); marketing (the ability of websites to be found through search engines and the relevance of descriptions to the links provided); content quality (grammar, frequency of information updates, material relevancy, and readability); and technology (download speed, quality of the programming code, and website infrastructure). Using these tools, we scored each website in each category. The composite of key factors in each category contributed to an overall “general usability” score for each website. An overall score was then calculated by applying a weighted percentage across all factors and was used for the final “overall usability” ranking. Results The category with the highest average score was technology, with a 0.82 (SD 0.068, SE 0.008). The lowest-performing category was content quality, with an average of 0.22 (SD 0.069, SE 0.008). As these numbers reflect weighted percentages as an integer, the higher the score, the greater the overall usability in that category. Conclusions Our data suggest that technology, on average, was the highest-scored variable among academic medical center websites. Because website functionality is essential to a user’s experience, it is justified that academic medical centers invest in optimal website performance. The overall lowest-scored variable was content quality. A potential reason for this may be that academic medical center websites are usually larger in size, making it difficult to monitor the increased quantity of content. An easy way to improve this variable is to conduct more frequent website audits to assess readability, grammar, and relevance. Marketing is another area in which these organizations have potential for improvement. Our recommendation is that organizations utilize search engine optimization techniques to improve their online visibility and discoverability.

Background: Universal access to assessment and treatment of mental health and learning disorders remains a significant and unmet need. There are many people without access to care because of economic, geographic, and cultural barriers, as well as the limited availability of clinical experts who could help advance our understanding and treatment of mental health. Objective: This study aims to create an open, configurable software platform to build clinical measures, mobile assessments, tasks, and interventions without programming expertise. Specifically, our primary requirements include an administrator interface for creating and scheduling recurring and customized questionnaires where end users receive and respond to scheduled notifications via an iOS or Android app on a mobile device. Such a platform would help relieve overwhelmed health systems and empower remote and disadvantaged subgroups in need of accurate and effective information, assessment, and care. This platform has the potential to advance scientific research by supporting the collection of data with instruments tailored to specific scientific questions from large, distributed, and diverse populations. Methods: We searched for products that satisfy these requirements. We designed and developed a new software platform called MindLogger, which exceeds the requirements. To demonstrate the platform’s configurability, we built multiple applets (collections of activities) within the MindLogger mobile app and deployed several of them, including a comprehensive set of assessments underway in a large-scale, longitudinal mental health study. Results: Of the hundreds of products we researched, we found 10 that met our primary requirements with 4 that support end-to-end encryption, 2 that enable restricted access to individual users’ data, 1 that provides open-source software, and none that satisfy all three. We compared features related to information presentation and data capture capabilities; privacy and security; and access to the product, code, and data. We successfully built MindLogger mobile and web applications, as well as web browser–based tools for building and editing new applets and for administering them to end users. MindLogger has end-to-end encryption, enables restricted access, is open source, and supports a variety of data collection features. One applet is currently collecting data from children and adolescents in our mental health study, and other applets are in different stages of testing and deployment for use in clinical and research settings. Conclusions: We demonstrated the flexibility and applicability of the MindLogger platform through its deployment in a large-scale, longitudinal, mobile mental health study and by building a variety of other mental health–related applets. With this release, we encourage a broad range of users to apply the MindLogger platform to create and test applets to advance health care and scientific research. We hope that increasing the availability of applets designed to assess and administer interventions will facilitate access to health care in the general population., Journal of Medical Internet Research, 23 (11), ISSN:1438-8871

Background Patient portals tethered to electronic health records systems have become attractive web platforms since the enacting of the Medicare Access and Children’s Health Insurance Program Reauthorization Act and the introduction of the Meaningful Use program in the United States. Patients can conveniently access their health records and seek consultation from providers through secure web portals. With increasing adoption and patient engagement, the volume of patient secure messages has risen substantially, which opens up new research and development opportunities for patient-centered care. Objective This study aims to develop a data model for patient secure messages based on the Fast Healthcare Interoperability Resources (FHIR) standard to identify and extract significant information. Methods We initiated the first draft of the data model by analyzing FHIR and manually reviewing 100 sentences randomly sampled from more than 2 million patient-generated secure messages obtained from the online patient portal at the Mayo Clinic Rochester between February 18, 2010, and December 31, 2017. We then annotated additional sets of 100 randomly selected sentences using the Multi-purpose Annotation Environment tool and updated the data model and annotation guideline iteratively until the interannotator agreement was satisfactory. We then created a larger corpus by annotating 1200 randomly selected sentences and calculated the frequency of the identified medical concepts in these sentences. Finally, we performed topic modeling analysis to learn the hidden topics of patient secure messages related to 3 highly mentioned microconcepts, namely, fatigue, prednisone, and patient visit, and to evaluate the proposed data model independently. Results The proposed data model has a 3-level hierarchical structure of health system concepts, including 3 macroconcepts, 28 mesoconcepts, and 85 microconcepts. Foundation and base macroconcepts comprise 33.99% (841/2474), clinical macroconcepts comprise 64.38% (1593/2474), and financial macroconcepts comprise 1.61% (40/2474) of the annotated corpus. The top 3 mesoconcepts among the 28 mesoconcepts are condition (505/2474, 20.41%), medication (424/2474, 17.13%), and practitioner (243/2474, 9.82%). Topic modeling identified hidden topics of patient secure messages related to fatigue, prednisone, and patient visit. A total of 89.2% (107/120) of the top-ranked topic keywords are actually the health concepts of the data model. Conclusions Our data model and annotated corpus enable us to identify and understand important medical concepts in patient secure messages and prepare us for further natural language processing analysis of such free texts. The data model could be potentially used to automatically identify other types of patient narratives, such as those in various social media and patient forums. In the future, we plan to develop a machine learning and natural language processing solution to enable automatic triaging solutions to reduce the workload of clinicians and perform more granular content analysis to understand patients’ needs and improve patient-centered care.

Background Depression and anxiety are the most common mental health conditions, and they were identified as leading contributors to global disability in 2016. People with these conditions rely on Web-based resources as a source of accurate health information, convenient and effective treatment, and essential social support. However, a recent systematic review revealed several potentially limiting difficulties that this group experiences online and also suggested that there is a partial understanding of these difficulties as only difficulties associated with neurocognitive, but not sociocognitive, deficits were identified. Therefore, this study fills this knowledge gap and contributes to a more robust and fuller understanding of the difficulties this group experiences online. Objective The objective of this study was to identify the difficulties people with depression and anxiety experience when using the Web and the Web activities that are most associated with the experience of difficulties. Methods The study employed data triangulation using face-to-face semistructured interviews with 21 participants affected by depression and anxiety and a comparison group (7 participants) without mental disorders (study 1) as well as a persona-based expert online survey with 21 mental health practitioners (MHPs) who treated people with depression and anxiety (study 2). Framework analysis for both studies proceeded through 5 stages: (1) familiarization, (2) identifying a thematic framework, (3) indexing, (4) charting, and (5) mapping and interpretation. Results In study 1, 167 difficulties were identified from the experiences of participants in the depression and anxiety group were discussed within the context of 81 Web activities, services, and features. From these, 4 themes and 12 subthemes describing the difficulties people with depression and anxiety experienced online were identified. Difficulties relating to the subtheme lack of control over access and usage were the most common difficulties experienced by participants in the depression and anxiety group (19/21). Sixteen difficulties identified from the experiences of participants in the comparison group were discussed within the context of 11 Web activities, services, and features. Most participants in the comparison group (6/7) contributed to the subtheme describing difficulties with unexpected and irrelevant content. In study 2, researchers identified 3 themes and 10 subthemes that described the perceived difficulties people with depression and anxiety might experience online as reported by MHPs. Practitioners linked these difficulties with 22 common impairments, limitations in activities of daily life, and diagnostic criteria associated with depression and anxiety. Conclusions People with depression and anxiety also experience difficulties when using the Web that are related to the sociocognitive deficits associated with their conditions. MHPs have a good awareness of the difficulties that people with depression and anxiety are likely to experience when using the Web. This investigation has contributed to a fuller understanding of these difficulties and provides innovative guidance on how to remove and reduce them for people with depression and anxiety when using the Web. International Registered Report Identifier (IRRID) RR2-10.1007/978-3-319-21006-3_3

Background Tools used to appraise the credibility of health information are time-consuming to apply and require context-specific expertise, limiting their use for quickly identifying and mitigating the spread of misinformation as it emerges. Objective The aim of this study was to estimate the proportion of vaccine-related Twitter posts linked to Web pages of low credibility and measure the potential reach of those posts. Methods Sampling from 143,003 unique vaccine-related Web pages shared on Twitter between January 2017 and March 2018, we used a 7-point checklist adapted from validated tools and guidelines to manually appraise the credibility of 474 Web pages. These were used to train several classifiers (random forests, support vector machines, and recurrent neural networks) using the text from a Web page to predict whether the information satisfies each of the 7 criteria. Estimating the credibility of all other Web pages, we used the follower network to estimate potential exposures relative to a credibility score defined by the 7-point checklist. Results The best-performing classifiers were able to distinguish between low, medium, and high credibility with an accuracy of 78% and labeled low-credibility Web pages with a precision of over 96%. Across the set of unique Web pages, 11.86% (16,961 of 143,003) were estimated as low credibility and they generated 9.34% (1.64 billion of 17.6 billion) of potential exposures. The 100 most popular links to low credibility Web pages were each potentially seen by an estimated 2 million to 80 million Twitter users globally. Conclusions The results indicate that although a small minority of low-credibility Web pages reach a large audience, low-credibility Web pages tend to reach fewer users than other Web pages overall and are more commonly shared within certain subpopulations. An automatic credibility appraisal tool may be useful for finding communities of users at higher risk of exposure to low-credibility vaccine communications.

Background: Wikipedia is one of the most consulted health resources in the world. Since the public is using health information from Wikipedia to make health care decisions, improving the quality of that health information is in the public interest. The open editable content design of Wikipedia and quality control processes in place provide an opportunity to add high-value, evidence-based information and take an active role in improving the health care information infrastructure. Objective: The aim of this project was to enhance Wikipedia health pages using high-quality, current research findings and track the persistence of those edits and number of page views after the changes to assess the reach of this initiative. Methods: We conducted Wikipedia Editathons with 3 different cohorts of Physical Therapy (PT) students to add high-quality health information to existing Wikipedia pages. Students synthesized best evidence information and updated and/or corrected existing Wikipedia entries on specific health pages. To evaluate the impact of these contributions, we examined two factors: (1) response to our contributions from the Wikipedia editing community, including number and type of subsequent edits as well as persistence of the student contributions and (2) number of page views by the public from the time of the page edits. Results: A total of 98 PT students in 3 different cohorts engaged in Editathons, editing 24 health pages. Of the 24 edits, 22 persisted at the end of the observation period (from time of entry to May 31, 2018) and received nearly 8 million page views. Each health page had an average of 354,724 page views. Conclusions: The Wikipedia Editathon is an effective way to continuously enhance the quality of health information available on Wikipedia. It is also an excellent way of bridging health technology with best-evidence medical facts and disseminating accurate, useful information to the public.

Background: Most research on how to enhance response rates in physician surveys has been done using paper surveys. Uncertainties remain regarding how to enhance response rates in Internet-based surveys. Objective: To evaluate the impact of a low-cost nonmonetary incentive and paper mail reminders (formal letter and postcard) on response rates in Internet-based physician surveys. Methods: We executed a factorial-design randomized experiment while conducting a nationally representative Internet-based physician survey. We invited 3966 physicians (randomly selected from a commercial database of all licensed US physicians) via email to complete an Internet-based survey. We used 2 randomly assigned email messages: one message offered a book upon survey completion, whereas the other did not mention the book but was otherwise identical. All nonrespondents received several email reminders. Some physicians were further assigned at random to receive 1 reminder via paper mail (either a postcard or a letter) or no paper reminder. The primary outcome of this study was the survey response rate. Results: Of the 3966 physicians who were invited, 451 (11.4%) responded to at least one survey question and 336 (8.5%) completed the entire survey. Of those who were offered a book, 345/2973 (11.6%) responded compared with 106/993 (10.7%) who were not offered a book (odds ratio 1.10, 95% CI 0.87-1.38, P=.42). Regarding the paper mail reminder, 168/1572 (10.7%) letter recipients, 148/1561 (9.5%) postcard recipients, and 69/767 (9.0%) email-only recipients responded (P=.35). The response rate for those receiving letters or postcards was similar (odds ratio 1.14, 95% CI 0.91-1.44, P=.26). Conclusions: Offering a modest nonmonetary incentive and sending a paper reminder did not improve survey response rate. Further research on how to enhance response rates in Internet-based physician surveys is needed. [J Med Internet Res 2016;18(9):e244]

Background: Twitter’s 140-character microblog posts are increasingly used to access information and facilitate discussions among health care professionals and between patients with chronic conditions and their caregivers. Recently, efforts have emerged to investigate the content of health care-related posts on Twitter. This marks a new area for researchers to investigate and apply content analysis (CA). In current infodemiology, infoveillance and digital disease detection research initiatives, quantitative and qualitative Twitter data are often combined, and there are no clear guidelines for researchers to follow when collecting and evaluating Twitter-driven content. Objective: The aim of this study was to identify studies on health care and social media that used Twitter feeds as a primary data source and CA as an analysis technique. We evaluated the resulting 18 studies based on a narrative review of previous methodological studies and textbooks to determine the criteria and main features of quantitative and qualitative CA. We then used the key features of CA and mixed-methods research designs to propose the combined content-analysis (CCA) model as a solid research framework for designing, conducting, and evaluating investigations of Twitter-driven content. Methods: We conducted a PubMed search to collect studies published between 2010 and 2014 that used CA to analyze health care-related tweets. The PubMed search and reference list checks of selected papers identified 21 papers. We excluded 3 papers and further analyzed 18. Results: Results suggest that the methods used in these studies were not purely quantitative or qualitative, and the mixed-methods design was not explicitly chosen for data collection and analysis. A solid research framework is needed for researchers who intend to analyze Twitter data through the use of CA. Conclusions: We propose the CCA model as a useful framework that provides a straightforward approach to guide Twitter-driven studies and that adds rigor to health care social media investigations. We provide suggestions for the use of the CCA model in elder care-related contexts. [J Med Internet Res 2016;18(3):e60]

Background: Social media is a popular online tool that allows users to communicate and exchange information. It allows digital content such as pictures, videos and websites to be shared, discussed, republished and endorsed by its users, their friends and businesses. Adverts can be posted and promoted to specific target audiences by demographics such as region, age or gender. Recruiting for health research is complex with strict requirement criteria imposed on the participants. Traditional research recruitment relies on flyers, newspaper adverts, radio and television broadcasts, letters, emails, website listings, and word of mouth. These methods are potentially poor at recruiting hard to reach demographics, can be slow and expensive. Recruitment via social media, in particular Facebook, may be faster and cheaper. Objective: The aim of this study was to systematically review the literature regarding the current use and success of Facebook to recruit participants for health research purposes. Methods: A literature review was completed in March 2017 in the English language using MEDLINE, EMBASE, Web of Science, PubMed, PsycInfo, Google Scholar, and a hand search of article references. Papers from the past 12 years were included and number of participants, recruitment period, number of impressions, cost per click or participant, and conversion rate extracted. Results: A total of 35 studies were identified from the United States (n=22), Australia (n=9), Canada (n=2), Japan (n=1), and Germany (n=1) and appraised using the Critical Appraisal Skills Programme (CASP) checklist. All focused on the feasibility of recruitment via Facebook, with some (n=10) also testing interventions, such as smoking cessation and depression reduction. Most recruited young age groups (16-24 years), with the remaining targeting specific demographics, for example, military veterans. Information from the 35 studies was analyzed with median values being 264 recruited participants, a 3-month recruitment period, 3.3 million impressions, cost per click of US $0.51, conversion rate of 4% (range 0.06-29.50), eligibility of 61% (range 17-100), and cost per participant of US $14.41. The studies showed success in penetrating hard to reach populations, finding the results representative of their control or comparison demographic, except for an over representation of young white women. Conclusions: There is growing evidence to suggest that Facebook is a useful recruitment tool and its use, therefore, should be considered when implementing future health research. When compared with traditional recruitment methods (print, radio, television, and email), benefits include reduced costs, shorter recruitment periods, better representation, and improved participant selection in young and hard to reach demographics. It however, remains limited by Internet access and the over representation of young white women. Future studies should recruit across all ages and explore recruitment via other forms of social media. [J Med Internet Res 2017;19(8):e290]

Background Reporting cumulative antimicrobial susceptibility testing data on a regular basis is crucial to inform antimicrobial resistance (AMR) action plans at local, national, and global levels. However, analyzing data and generating a report are time consuming and often require trained personnel. Objective This study aimed to develop and test an application that can support a local hospital to analyze routinely collected electronic data independently and generate AMR surveillance reports rapidly. Methods An offline application to generate standardized AMR surveillance reports from routinely available microbiology and hospital data files was written in the R programming language (R Project for Statistical Computing). The application can be run by double clicking on the application file without any further user input. The data analysis procedure and report content were developed based on the recommendations of the World Health Organization Global Antimicrobial Resistance Surveillance System (WHO GLASS). The application was tested on Microsoft Windows 10 and 7 using open access example data sets. We then independently tested the application in seven hospitals in Cambodia, Lao People’s Democratic Republic, Myanmar, Nepal, Thailand, the United Kingdom, and Vietnam. Results We developed the AutoMated tool for Antimicrobial resistance Surveillance System (AMASS), which can support clinical microbiology laboratories to analyze their microbiology and hospital data files (in CSV or Excel format) onsite and promptly generate AMR surveillance reports (in PDF and CSV formats). The data files could be those exported from WHONET or other laboratory information systems. The automatically generated reports contain only summary data without patient identifiers. The AMASS application is downloadable from https://www.amass.website/. The participating hospitals tested the application and deposited their AMR surveillance reports in an open access data repository. Conclusions The AMASS is a useful tool to support the generation and sharing of AMR surveillance reports.

Background The German Biobank Alliance (GBA) aims to establish a cross-site biobank network. For this endeavor, the so-called Sample Locator, a federated search tool for biospecimens and related data, has been developed, forming the heart of its information technology (IT) infrastructure. Objective To ensure the sustainable use of such a tool, we included researchers as participants in an end user–based usability evaluation. Methods To develop a prototype ready for evaluation, we needed input from GBA IT experts. Thus, we conducted a 2-day workshop with 8 GBA IT team members. The focus was on the respective steps of a user-centered design process. With the acquired knowledge, the participants designed low-fidelity mock-ups. The main ideas of these mock-ups were discussed, extracted, and summarized into a comprehensive prototype using Microsoft PowerPoint. Furthermore, we created a questionnaire concerning the usability of the prototype, including the System Usability Scale (SUS), questions on negative and positive aspects, and typical tasks to be fulfilled with the tool. Subsequently, the prototype was pretested on the basis of this questionnaire with researchers who have a biobank background. Based on this preliminary work, the usability analysis was ultimately carried out with researchers and the results were evaluated. Results Altogether, 27 researchers familiar with sample requests evaluated the prototype. The analysis of the feedback certified a good usability, given that the Sample Locator prototype was seen as intuitive and user-friendly by 74% (20/27) of the participants. The total SUS score by the 25 persons that completed the questionnaire was 80.4, indicating good system usability. Still, the evaluation provided useful advice on optimization potential (eg, offering a help function). Conclusions The findings of this usability analysis indicate that the considerations regarding a user-friendly application that have been made in the development process so far strongly coincide with the perception of the study participants. Nevertheless, it was important to engage prospective end users to ensure that the previous development is going in the desired direction and that the Sample Locator will be used in the future. The user comments and suggestions for improvement will be considered in upcoming iterations for refinement.

BackgroundWeb-based interventions have been shown to be effective for the treatment of depression. However, interventions are often complex and include a variety of elements, making it difficult to identify the most effective component(s).ObjectiveThe aim of this pilot study was to shed light on mechanisms in the online treatment of depression by comparing a single-module, fully automated intervention for depression (internet-based behavioral activation [iBA]) to a nonoverlapping active control intervention and a nonactive control group.MethodsWe assessed 104 people with at least mild depressive symptoms (Patient Health Questionnaire-9, >4) via the internet at baseline (t0) and 2 weeks (t1) and 4 weeks (t2) later. After the t0 assessment, participants were randomly allocated to one of three groups: (1) iBA (n=37), (2) active control using a brief internet-based mindfulness intervention (iMBI, n=32), or (3) care as usual (CAU, n=35). The primary outcome was improvement in depressive symptoms, as measured using the Patient Health Questionnaire-9. Secondary parameters included changes in activity, dysfunctional attitudes, and quality of lifeResultsWhile groups did not differ regarding the change in depression from t0 to t1 (ηp2=.007, P=.746) or t0 to t2 (ηp2=.008, P=.735), iBA was associated with a larger decrease in dysfunctional attitudes from t0 to t2 in comparison to CAU (ηp2=.053, P=.04) and a larger increase in activity from t0 to t1 than the pooled control groups (ηp2=.060, P=.02). A change in depression from t0 to t2 was mediated by a change in activity from t0 to t1. At t1, 22% (6/27) of the participants in the iBA group and 12% (3/25) of the participants in the iMBI group indicated that they did not use the intervention.ConclusionsAlthough we did not find support for the short-term efficacy of the single-module iBA regarding depression, long-term effects are still conceivable, potentially initiated by changes in secondary outcomes. Future studies should use a longer intervention and follow-up interval.Trial RegistrationDKRS (#DRKS00011562)

Background: Long-term conditions and their concomitant management place considerable pressure on patients, communities, and health care systems worldwide. International clinical guidelines on the majority of long-term conditions recommend the inclusion of self-management programs in routine management. Self-management programs have been associated with improved health outcomes; however, the successful and sustainable transfer of research programs into clinical practice has been inconsistent. Recent developments in mobile technology, such as mobile phone and tablet computer apps, could help in developing a platform for the delivery of self-management interventions that are adaptable, of low cost, and easily accessible. Objective: We conducted a systematic review to assess the effectiveness of mobile phone and tablet apps in self-management of key symptoms of long-term conditions. Methods: We searched PubMed, Embase, EBSCO databases, the Cochrane Library, and The Joanna Briggs Institute Library for randomized controlled trials that assessed the effectiveness of mobile phone and tablet apps in self-management of diabetes mellitus, cardiovascular disease, and chronic lung diseases from 2005–2016. We searched registers of current and ongoing trials, as well as the gray literature. We then checked the reference lists of all primary studies and review papers for additional references. The last search was run in February 2016. Results: Of the 9 papers we reviewed, 6 of the interventions demonstrated a statistically significant improvement in the primary measure of clinical outcome. Where the intervention comprised an app only, 3 studies demonstrated a statistically significant improvement. Interventions to address diabetes mellitus (5/9) were the most common, followed by chronic lung disease (3/9) and cardiovascular disease (1/9). A total of 3 studies included multiple intervention groups using permutations of an intervention involving an app. The duration of the intervention ranged from 6 weeks to 1 year, and final follow-up data ranged from 3 months to 1 year. Sample size ranged from 48 to 288 participants. Conclusions: The evidence indicates the potential of apps in improving symptom management through self-management interventions. The use of apps in mHealth has the potential to improve health outcomes among those living with chronic diseases through enhanced symptom control. Further innovation, optimization, and rigorous research around the potential of apps in mHealth technology will move the field toward the reality of improved health care delivery and outcomes. [J Med Internet Res 2016;18(5):e97]

Background: Research networking systems hold great promise for helping biomedical scientists identify collaborators with the expertise needed to build interdisciplinary teams. Although efforts to date have focused primarily on collecting and aggregating information, less attention has been paid to the design of end-user tools for using these collections to identify collaborators. To be effective, collaborator search tools must provide researchers with easy access to information relevant to their collaboration needs. Objective: The aim was to study user requirements and preferences for research networking system collaborator search tools and to design and evaluate a functional prototype. Methods: Paper prototypes exploring possible interface designs were presented to 18 participants in semistructured interviews aimed at eliciting collaborator search needs. Interview data were coded and analyzed to identify recurrent themes and related software requirements. Analysis results and elements from paper prototypes were used to design a Web-based prototype using the D3 JavaScript library and VIVO data. Preliminary usability studies asked 20 participants to use the tool and to provide feedback through semistructured interviews and completion of the System Usability Scale (SUS). Results: Initial interviews identified consensus regarding several novel requirements for collaborator search tools, including chronological display of publication and research funding information, the need for conjunctive keyword searches, and tools for tracking candidate collaborators. Participant responses were positive (SUS score: mean 76.4%, SD 13.9). Opportunities for improving the interface design were identified. Conclusions: Interactive, timeline-based displays that support comparison of researcher productivity in funding and publication have the potential to effectively support searching for collaborators. Further refinement and longitudinal studies may be needed to better understand the implications of collaborator search tools for researcher workflows. [J Med Internet Res 2014;16(11):e244]

Background The recent surge in commercially available wearable technology has allowed real-time self-monitoring of behavior (eg, physical activity) and physiology (eg, glucose levels). However, there is limited neuroimaging work (ie, functional magnetic resonance imaging [fMRI]) to identify how people’s brains respond to receiving this personalized health feedback and how this impacts subsequent behavior. Objective Identify regions of the brain activated and examine associations between activation and behavior. Methods This was a pilot study to assess physical activity, sedentary time, and glucose levels over 14 days in 33 adults (aged 30 to 60 years). Extracted accelerometry, inclinometry, and interstitial glucose data informed the construction of personalized feedback messages (eg, average number of steps per day). These messages were subsequently presented visually to participants during fMRI. Participant physical activity levels and sedentary time were assessed again for 8 days following exposure to this personalized feedback. Results Independent tests identified significant activations within the prefrontal cortex in response to glucose feedback compared with behavioral feedback (P

Background: The translation of Web-based physical activity intervention research into the real world is lacking and becoming increasingly important. Objective: To compare usage and effectiveness, in real-world settings, of a traditional Web 1.0 Web-based physical activity intervention, providing limited interactivity, to a Web 2.0 Web-based physical activity intervention that includes interactive features, such as social networking (ie, status updates, online “friends,” and personalized profile pages), blogs, and Google Maps mash-ups. Methods: Adults spontaneously signing up for the freely available 10,000 Steps website were randomized to the 10,000 Steps website (Web 1.0) or the newly developed WALK 2.0 website (Web 2.0). Physical activity (Active Australia Survey), quality of life (RAND 36), and body mass index (BMI) were assessed at baseline, 3 months, and 12 months. Website usage was measured continuously. Analyses of covariance were used to assess change over time in continuous outcome measures. Multiple imputation was used to deal with missing data. Results: A total of 1328 participants completed baseline assessments. Only 3-month outcomes (224 completers) were analyzed due to high attrition at 12 months (77 completers). Web 2.0 group participants increased physical activity by 92.8 minutes per week more than those in the Web 1.0 group (95% CI 28.8-156.8; P=.005); their BMI values also decreased more (–1.03 kg/m2, 95% CI –1.65 to -0.41; P=.001). For quality of life, only the physical functioning domain score significantly improved more in the Web 2.0 group (3.6, 95% CI 1.7-5.5; P

Background: Recruitment is often a difficult and costly part of any human research study. Social media and other emerging means of mass communication hold promise as means to complement traditional strategies used for recruiting participants because they can reach a large number of people in a short amount of time. With the ability to target a specified audience, paid Facebook advertisements have potential to reach future research participants of a specific demographic. This paper describes the experiences of a randomized controlled trial in Edmonton, Alberta, attempting to recruit healthy pregnant women between 8 and 20 weeks’ gestation for participation in a prenatal study. Various traditional recruitment approaches, in addition to paid Facebook advertisements were trialed. Objective: To evaluate the effectiveness of paid advertisements on Facebook as a platform for recruiting pregnant women to a randomized controlled trial in comparison with traditional recruitment approaches. Methods: Recruitment using traditional approaches occurred for 7 months, whereas Facebook advertisements ran for a total of 26 days. Interested women were prompted to contact the study staff for a screening call to determine study eligibility. Costs associated with each recruitment approach were recorded and used to calculate the cost to recruit eligible participants. Performance of Facebook advertisements was monitored using Facebook Ads Manager. Results: Of the 115 women included, 39.1% (n=45) of the women who contacted study staff heard about the study through Facebook, whereas 60.9% (n=70) of them heard about it through traditional recruitment approaches. During the 215 days (~7 months) that the traditional approaches were used, the average rate of interest was 0.3 (0.2) women/day, whereas the 26 days of Facebook advertisements resulted in an average rate of interest of 2.8 (1.7) women/day. Facebook advertisements cost Can $506.91 with a cost per eligible participant of Cad $20.28. In comparison, the traditional approaches cost Cad $1087, with approximately Cad $24.15 per eligible participant. Demographic characteristics of women were similar between the 2 recruitment methods except that women recruited using Facebook were significantly earlier in their pregnancy than those recruited using traditional approaches (P

Background: The academic scandal on a study on stimulus‑triggered acquisition of pluripotency (STAP) cells in Japan in 2014 involved suspicions of scientific misconduct by the lead author of the study after the paper had been reviewed on a peer‑review website. This study investigated the discussions on STAP cells on Twitter and content of newspaper articles in an attempt to assess the role of social compared with traditional media in scientific peer review. Objective: This study examined Twitter utilization in scientific peer review on STAP cells misconduct. Methods: Searches for tweets and newspaper articles containing the term “STAP cells” were carried out through Twitter’s search engine and Nikkei Telecom database, respectively. The search period was from January 1 to July 1, 2014. The nouns appearing in the “top tweets” and newspaper articles were extracted through a morphological analysis, and their frequency of appearance and changes over time were investigated. Results: The total numbers of top tweets and newspaper articles containing the term were 134,958 and 1646, respectively. Negative words concerning STAP cells began to appear on Twitter by February 9-15, 2014, or 3 weeks after Obokata presented a paper on STAP cells. The number of negative words in newspaper articles gradually increased beginning in the week of March 12-18, 2014. A total of 1000 tweets were randomly selected, and they were found to contain STAP-related opinions (43.3%, 433/1000), links to news sites and other sources (41.4%, 414/1000), false scientific or medical claims (8.9%, 89/1000), and topics unrelated to STAP (6.4%, 64/1000). Conclusions: The discussion on scientific misconduct during the STAP cells scandal took place at an earlier stage on Twitter than in newspapers, a traditional medium. [J Med Internet Res 2017;19(2):e57]

Background: Electronic health (eHealth) literacy is an important skill that allows patients to navigate intelligibly through the vast, often misleading Web-based world. Although eHealth literacy has been investigated in general and specific demographic populations, it has not yet been analyzed on users of online health communities (OHCs). Evidence shows that OHCs are important Web 2.0 applications for patients for managing their health, but at the same time, warnings have been expressed regarding the quality and relevance of shared information. No studies exist that investigate levels of eHealth literacy among users of OHCs and differences in eHealth literacy between different types of users. Objective: The study aimed to investigate eHealth literacy across different types of users of OHCs based on a revised and extended eHealth literacy scale (eHEALS). Methods: The study was based on a cross-sectional Web survey on a simple random sample of 15,000 registered users of the most popular general OHC in Slovenia. The final sample comprised 644 users of the studied OHC. An extended eHEALS (eHEALS-E) was tested with factor analytical procedures, whereas user types were identified with a hierarchical clustering algorithm. The research question was analyzed with analysis of variance (ANOVA) procedure and pairwise comparison tests. Results: Factor analysis of the revised and extended eHEALS revealed six dimensions: awareness of sources, recognizing quality and meaning, understanding information, perceived efficiency, validating information, and being smart on the Net. The factor solution demonstrates a good fit to the data (root mean square error of approximation [RMSEA]=.059). The most developed dimension of eHEALS-E is awareness of different Internet sources (mean=3.98, standard deviation [SD]=0.61), whereas the least developed is understanding information (mean=3.11, SD=0.75). Clustering resulted in four user types: active help-seekers (48.3%, 311/644), lurkers (31.8%, 205/644), core relational users (16.9%, 109/644), and low-engaged users (3%, 19/644). Analysis of the research question showed statistically significant differences among user types across all six dimensions of eHEALS-E. Most notably, core relational users performed worse than lurkers on the validating information dimension (P=.01) and worse than active help-seekers on the being smart on the Net dimension (P=.05). Active help-seekers have the highest scores in all dimensions of the eHEALS-E, whereas low-engaged users have statistically significantly lower scores on all dimensions of the eHEALS-E in comparison with the other groups. Conclusions: Those who are looking for advice and support in OHCs by making queries are well equipped with eHealth literacy skills to filter potential misinformation and detect bad advice. However, core relational users (who produce the most content in OHCs) have less-developed skills for cross-validating the information obtained and navigating successfully through the perils of the online world. Site managers should monitor their activity to avoid the spread of misinformation that might lead to unhealthy practices. [J Med Internet Res 2017;19(10):e331]

Background: Cervical cancer is the second most common cancer among women under 45 years of age. To deal with the decrease of smear test coverage in the United Kingdom, a Twitter campaign called #SmearForSmear has been launched in 2015 for the European Cervical Cancer Prevention Week. Its aim was to encourage women to take a selfie showing their lipstick going over the edge and post it on Twitter with a raising awareness message promoting cervical cancer screening. The estimated audience was 500 million people. Other public health campaigns have been launched on social media such as Movember to encourage participation and self-engagement. Their result was unsatisfactory as their aim had been diluted to become mainly a social buzz. Objective: The objectives of this study were to identify the tweets delivering a raising awareness message promoting cervical cancer screening (sensitizing tweets) and to understand the characteristics of Twitter users posting about this campaign. Methods: We conducted a 3-step content analysis of the English tweets tagged #SmearForSmear posted on Twitter for the 2015 European Cervical Cancer Prevention Week. Data were collected using the Twitter application programming interface. Their extraction was based on an analysis grid generated by 2 independent researchers using a thematic analysis, validated by a strong Cohen kappa coefficient. A total of 7 themes were coded for sensitizing tweets and 14 for Twitter users’ status. Verbatims were thematically and then statistically analyzed. Results: A total of 3019 tweets were collected and 1881 were analyzed. Moreover, 69.96% of tweets had been posted by people living in the United Kingdom. A total of 57.36% of users were women, and sex was unknown in 35.99% of cases. In addition, 54.44% of the users had posted at least one selfie with smeared lipstick. Furthermore, 32.32% of tweets were sensitizing. Independent factors associated with posting sensitizing tweets were women who experienced an abnormal smear test (OR [odds ratio] 13.456, 95% CI 3.101-58.378, P

Background: National and subnational level surveys are important for monitoring disease burden, prioritizing resource allocation, and evaluating public health policies. As mobile phone access and ownership become more common globally, mobile phone surveys (MPSs) offer an opportunity to supplement traditional public health household surveys. Objective: The objective of this study was to systematically review the current landscape of MPSs to collect population-level estimates in low- and middle-income countries (LMICs). Methods: Primary and gray literature from 7 online databases were systematically searched for studies that deployed MPSs to collect population-level estimates. Titles and abstracts were screened on primary inclusion and exclusion criteria by two research assistants. Articles that met primary screening requirements were read in full and screened for secondary eligibility criteria. Articles included in review were grouped into the following three categories by their survey modality: (1) interactive voice response (IVR), (2) short message service (SMS), and (3) human operator or computer-assisted telephone interviews (CATI). Data were abstracted by two research assistants. The conduct and reporting of the review conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Results: A total of 6625 articles were identified through the literature review. Overall, 11 articles were identified that contained 19 MPS (CATI, IVR, or SMS) surveys to collect population-level estimates across a range of topics. MPSs were used in Latin America (n=8), the Middle East (n=1), South Asia (n=2), and sub-Saharan Africa (n=8). Nine articles presented results for 10 CATI surveys (10/19, 53%). Two articles discussed the findings of 6 IVR surveys (6/19, 32%). Three SMS surveys were identified from 2 articles (3/19, 16%). Approximately 63% (12/19) of MPS were delivered to mobile phone numbers collected from previously administered household surveys. The majority of MPS (11/19, 58%) were panel surveys where a cohort of participants, who often were provided a mobile phone upon a face-to-face enrollment, were surveyed multiple times. Conclusions: Very few reports of population-level MPS were identified. Of the MPS that were identified, the majority of surveys were conducted using CATI. Due to the limited number of identified IVR and SMS surveys, the relative advantages and disadvantages among the three survey modalities cannot be adequately assessed. The majority of MPS were sent to mobile phone numbers that were collected from a previously administered household survey. There is limited evidence on whether a random digit dialing (RDD) approach or a simple random sample of mobile network provided list of numbers can produce a population representative survey. [J Med Internet Res 2017;19(5):e139]

Background: Body listening, described as the act of paying attention to the body’s signals and cues, can be an important component of long-term health management. Objective: The aim of this study was to introduce and evaluate the Body Listening Project, an innovative effort to engage the public in the creation of a public resource—to leverage collective wisdom in the health domain. This project involved a website where people could contribute their experiences of and dialogue with others concerning body listening and self-management. This article presents an analysis of the tags contributed, with a focus on the value of these tags for knowledge organization and incorporation into consumer-friendly health information retrieval systems. Methods: First, we performed content analysis of the tags contributed, identifying a set of categories and refining the relational structure of the categories to develop a preliminary classification scheme, the Body Listening and Self-Management Taxonomy. Second, we compared the concepts in the Body Listening and Self-Management Taxonomy with concepts that were automatically identified from an extant health knowledge resource, the Unified Medical Language System (UMLS), to better characterize the information that participants contributed. Third, we employed visualization techniques to explore the concept space of the tags. A correlation matrix, based on the extent to which categories tended to be assigned to the same tags, was used to study the interrelatedness of the taxonomy categories. Then a network visualization was used to investigate structural relationships among the categories in the taxonomy. Results: First, we proposed a taxonomy called the Body Listening and Self-Management Taxonomy, with four meta-level categories: (1) health management strategies, (2) concepts and states, (3) influencers, and (4) health-related information behavior. This taxonomy could inform future efforts to organize knowledge and content of this subject matter. Second, we compared the categories from this taxonomy with the UMLS concepts that were identified. Though the UMLS offers benefits such as speed and breadth of coverage, the Body Listening and Self-Management Taxonomy is more consumer-centric. Third, the correlation matrix and network visualization demonstrated that there are natural areas of ambiguity and semantic relatedness in the meanings of the concepts in the Body Listening and Self-Management Taxonomy. Use of these visualizations can be helpful in practice settings, to help library and information science practitioners understand and resolve potential challenges in classification; in research, to characterize the structure of the conceptual space of health management; and in the development of consumer-centric health information retrieval systems. Conclusions: A participatory platform can be employed to collect data concerning patient experiences of health management, which can in turn be used to develop new health knowledge resources or augment existing ones, as well as be incorporated into consumer-centric health information systems. [J Med Internet Res 2017;19(9):e292]

Background: Little cigar and cigarillo use is becoming more prevalent in the United States and elsewhere, with implications for public health. As little cigar and cigarillo use grows in popularity, big social media data (eg, Instagram, Google Web Search, Twitter) can be used to capture and document the context in which individuals use, and are marketed, these tobacco products. Big social media data may allow people to organically demonstrate how and why they use little cigars and cigarillos, unprimed by a researcher, without instrument bias and at low costs. Objective: This study characterized Swisher (the most popular brand of cigars in the United States, controlling over 75% of the market share) little cigar- and cigarillo-related posts on Instagram to inform the design of tobacco education campaigns and the development of future tobacco control efforts, and to demonstrate the utility in using big social media data in understanding health behaviors. Methods: We collected images from Instagram, an image-based social media app allowing users to capture, customize, and post photos on the Internet with over 400 million active users. Inclusion criteria for this study consisted of an Instagram post with the hashtag “#swisher”. We established rules for coding themes of images. Results: Of 1967 images collected, 486 (24.71%) were marijuana related, 348 (17.69%) were of tobacco products or promotional material, 324 (16.47%) showed individuals smoking, 225 (11.44%) were memes, and 584 (29.69%) were classified as other (eg, selfies, food, sexually explicit images). Of the marijuana-related images, 157/486 (32.3%) contained a Swisher wrapper, indicating that a Swisher product was used in blunt making, which involves hollowing out a cigar and refilling it with marijuana. Conclusions: Images from Instagram may be used to complement and extend the study of health behaviors including tobacco use. Images may be as valuable as, or more valuable than, words from other social media platforms alone. Posts on Instagram showing Swisher products, including blunt making, could add to the normalization of little cigar and cigarillo use and is an area of future research. Tobacco control researchers should design social media campaigns to combat smoking imagery found on popular sites such as Instagram. [J Med Internet Res 2017;19(7):e255]

Background: As a legal obligation, the Dutch government publishes online information about tobacco additives to make sure that it is publicly available. Little is known about the influence this website (”tabakinfo”) has on visitors and how the website is evaluated by them. Objective: This study assesses how visitors use the website and its effect on their knowledge, risk perception, attitude, and smoking behavior. The study will also assess how the website is evaluated by visitors using a sample of the Dutch general population, including smokers and nonsmokers. Methods: A randomized controlled trial was conducted, recruiting participants from an online panel. At baseline, participants (N=672) were asked to fill out an online questionnaire about tobacco additives. Next, participants were randomly allocated to either one of two experimental groups and invited to visit the website providing information about tobacco additives (either with or without a database containing product-specific information) or to a control group that had no access to the website. After 3 months, follow-up measurements took place. Results: At follow-up (n=492), no statistically significant differences were found for knowledge, risk perception, attitude, or smoking behavior between the intervention and control groups. Website visits were positively related to younger participants (B=–0.07, 95% CI –0.12 to –0.01; t11=–2.43, P=.02) and having a low risk perception toward tobacco additives (B=–0.32, 95% CI –0.63 to –0.02; t11=–2.07, P=.04). In comparison, having a lower education (B=–0.67, 95% CI –1.14 to –0.17; t11=–2.65, P=.01) was a significant predictor for making less use of the website. Furthermore, the website was evaluated less positively by smokers compared to nonsmokers (t324=–3.55, P

Background: The most popular social networking site in the United States is Facebook, an online forum where circles of friends create, share, and interact with each other’s content in a nonpublic way. Objective: Our objectives were to understand (1) the most commonly used terms and phrases relating to breast cancer screening, (2) the most commonly shared website links that other women interacted with, and (3) the most commonly shared website links, by age groups. Methods: We used a novel proprietary tool from Facebook to analyze all of the more than 1.7 million unique interactions (comments on stories, reshares, and emoji reactions) and stories associated with breast cancer screening keywords that were generated by more than 1.1 million unique female Facebook users over the 1 month between November 15 and December 15, 2016. We report frequency distributions of the most popular shared Web content by age group and keywords. Results: On average, each of 59,000 unique stories during the month was reshared 1.5 times, commented on nearly 8 times, and reacted to more than 20 times by other users. Posted stories were most often authored by women aged 45-54 years. Users shared, reshared, commented on, and reacted to website links predominantly to e-commerce sites (12,200/1.7 million, 36% of all the most popular links), celebrity news (n=8800, 26%), and major advocacy organizations (n=4900, 15%; almost all accounted for by the American Cancer Society breast cancer site). Conclusions: On Facebook, women shared and reacted to links to commercial and informative websites regarding breast cancer and screening. This information could inform patient outreach regarding breast cancer screening, indirectly through better understanding of key issues, and directly through understanding avenues for paid messaging to women authoring and reacting to content in this space. [J Med Internet Res 2017;19(6):e201]

Background: Given the growing interest in mobile data collection due to the proliferation of mobile phone ownership and network coverage in low- and middle-income countries (LMICs), we synthesized the evidence comparing estimates of health outcomes from multiple modes of data collection. In particular, we reviewed studies that compared a mode of remote data collection with at least one other mode of data collection to identify mode effects and areas for further research. Objective: The study systematically reviewed and summarized the findings from articles and reports that compare a mode of remote data collection to at least one other mode. The aim of this synthesis was to assess the reliability and accuracy of results. Methods: Seven online databases were systematically searched for primary and grey literature pertaining to remote data collection in LMICs. Remote data collection included interactive voice response (IVR), computer-assisted telephone interviews (CATI), short message service (SMS), self-administered questionnaires (SAQ), and Web surveys. Two authors of this study reviewed the abstracts to identify articles which met the primary inclusion criteria. These criteria required that the survey collected the data from the respondent via mobile phone or landline. Articles that met the primary screening criteria were read in full and were screened using secondary inclusion criteria. The four secondary inclusion criteria were that two or more modes of data collection were compared, at least one mode of data collection in the study was a mobile phone survey, the study had to be conducted in a LMIC, and finally, the study should include a health component. Results: Of the 11,568 articles screened, 10 articles were included in this study. Seven distinct modes of remote data collection were identified: CATI, SMS (singular sitting and modular design), IVR, SAQ, and Web surveys (mobile phone and personal computer). CATI was the most frequent remote mode (n=5 articles). Of the three in-person modes (face-to-face [FTF], in-person SAQ, and in-person IVR), FTF was the most common (n=11) mode. The 10 articles made 25 mode comparisons, of which 12 comparisons were from a single article. Six of the 10 articles included sensitive questions. Conclusions: This literature review summarizes the existing research about remote data collection in LMICs. Due to both heterogeneity of outcomes and the limited number of comparisons, this literature review is best positioned to present the current evidence and knowledge gaps rather than attempt to draw conclusions. In order to advance the field of remote data collection, studies that employ standardized sampling methodologies and study designs are necessary to evaluate the potential for differences by survey modality. [J Med Internet Res 2017;19(5):e140]

Background: Low back pain (LBP) is a common cause of disability and is ranked as the most burdensome health condition globally. Self-management, including components on increased knowledge, monitoring of symptoms, and physical activity, are consistently recommended in clinical guidelines as cost-effective strategies for LBP management and there is increasing interest in the potential role of digital health. Objective: The study aimed to synthesize and critically appraise published evidence concerning the use of interactive digital interventions to support self-management of LBP. The following specific questions were examined: (1) What are the key components of digital self-management interventions for LBP, including theoretical underpinnings? (2) What outcome measures have been used in randomized trials of digital self-management interventions in LBP and what effect, if any, did the intervention have on these? and (3) What specific characteristics or components, if any, of interventions appear to be associated with beneficial outcomes? Methods: Bibliographic databases searched from 2000 to March 2016 included Medline, Embase, CINAHL, PsycINFO, Cochrane Library, DoPHER and TRoPHI, Social Science Citation Index, and Science Citation Index. Reference and citation searching was also undertaken. Search strategy combined the following concepts: (1) back pain, (2) digital intervention, and (3) self-management. Only randomized controlled trial (RCT) protocols or completed RCTs involving adults with LBP published in peer-reviewed journals were included. Two reviewers independently screened titles and abstracts, full-text articles, extracted data, and assessed risk of bias using Cochrane risk of bias tool. An independent third reviewer adjudicated on disagreements. Data were synthesized narratively. Results: Of the total 7014 references identified, 11 were included, describing 9 studies: 6 completed RCTs and 3 protocols for future RCTs. The completed RCTs included a total of 2706 participants (range of 114-1343 participants per study) and varied considerably in the nature and delivery of the interventions, the duration/definition of LBP, the outcomes measured, and the effectiveness of the interventions. Participants were generally white, middle aged, and in 5 of 6 RCT reports, the majority were female and most reported educational level as time at college or higher. Only one study reported between-group differences in favor of the digital intervention. There was considerable variation in the extent of reporting the characteristics, components, and theories underpinning each intervention. None of the studies showed evidence of harm. Conclusions: The literature is extremely heterogeneous, making it difficult to understand what might work best, for whom, and in what circumstances. Participants were predominantly female, white, well educated, and middle aged, and thus the wider applicability of digital self-management interventions remains uncertain. No information on cost-effectiveness was reported. The evidence base for interactive digital interventions to support patient self-management of LBP remains weak. [J Med Internet Res 2017;19(5):e179]

Background: Twitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks. Objectives: This study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California. Methods: From a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities. Results: The modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75% (46/61) of the communities in the San Francisco Bay Area and 71% (41/58) in Greater Los Angeles. Conclusions: The most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the online surveillance of cannabis dispensaries and consumer markets over time. [J Med Internet Res 2017;19(7):e236]

Background: Electronic questionnaires can ease data collection in randomized controlled trials (RCTs) in clinical practice. We found no existing software that could automate the sending of emails to participants enrolled into an RCT at different study participant inclusion time points. Objective: Our aim was to develop suitable software to facilitate data collection in an ongoing multicenter RCT of low back pain (the Acuback study). For the Acuback study, we determined that we would need to send a total of 5130 emails to 270 patients recruited at different centers and at 19 different time points. Methods: The first version of the software was tested in a pilot study in November 2013 but was unable to deliver multiuser or Web-based access. We resolved these shortcomings in the next version, which we tested on the Web in February 2014. Our new version was able to schedule and send the required emails in the full-scale Acuback trial that started in March 2014. The system architecture evolved through an iterative, inductive process between the project study leader and the software programmer. The program was tested and updated when errors occurred. To evaluate the development of the software, we used a logbook, a research assistant dialogue, and Acuback trial participant queries. Results: We have developed a Web-based app, Survey Email Scheduling and Monitoring in eRCTs (SESAMe), that monitors responses in electronic surveys and sends reminders by emails or text messages (short message service, SMS) to participants. The overall response rate for the 19 surveys in the Acuback study increased from 76.4% (655/857) before we introduced reminders to 93.11% (1149/1234) after the new function (P

BackgroundIn recent years, people who sought direct-to-consumer genetic testing services have been increasingly confronted with an unprecedented amount of personal genomic information, which influences their decisions, emotional state, and well-being. However, these users of direct-to-consumer genetic services, who vary in their education and interests, frequently have little relevant experience or tools for understanding, reasoning about, and interacting with their personal genomic data. Online interactive techniques can play a central role in making personal genomic data useful for these users. ObjectiveWe sought to (1) identify the needs of diverse users as they make sense of their personal genomic data, (2) consequently develop effective interactive visualizations of genomic trait data to address these users’ needs, and (3) evaluate the effectiveness of the developed visualizations in facilitating comprehension. MethodsThe first two user studies, conducted with 63 volunteers in the Personal Genome Project and with 36 personal genomic users who participated in a design workshop, respectively, employed surveys and interviews to identify the needs and expectations of diverse users. Building on the two initial studies, the third study was conducted with 730 Amazon Mechanical Turk users and employed a controlled experimental design to examine the effectiveness of different design interventions on user comprehension. ResultsThe first two studies identified searching, comparing, sharing, and organizing data as fundamental to users’ understanding of personal genomic data. The third study demonstrated that interactive and visual design interventions could improve the understandability of personal genomic reports for consumers. In particular, results showed that a new interactive bubble chart visualization designed for the study resulted in the highest comprehension scores, as well as the highest perceived comprehension scores. These scores were significantly higher than scores received using the industry standard tabular reports currently used for communicating personal genomic information. ConclusionsDrawing on multiple research methods and populations, the findings of the studies reported in this paper offer deep understanding of users’ needs and practices, and demonstrate that interactive online design interventions can improve the understandability of personal genomic reports for consumers. We discuss implications for designers and researchers.

BackgroundWikipedia is a collaboratively edited encyclopedia. One of the most popular websites on the Internet, it is known to be a frequently used source of health care information by both professionals and the lay public. ObjectiveThis paper quantifies the production and consumption of Wikipedia’s medical content along 4 dimensions. First, we measured the amount of medical content in both articles and bytes and, second, the citations that supported that content. Third, we analyzed the medical readership against that of other health care websites between Wikipedia’s natural language editions and its relationship with disease prevalence. Fourth, we surveyed the quantity/characteristics of Wikipedia’s medical contributors, including year-over-year participation trends and editor demographics. MethodsUsing a well-defined categorization infrastructure, we identified medically pertinent English-language Wikipedia articles and links to their foreign language equivalents. With these, Wikipedia can be queried to produce metadata and full texts for entire article histories. Wikipedia also makes available hourly reports that aggregate reader traffic at per-article granularity. An online survey was used to determine the background of contributors. Standard mining and visualization techniques (eg, aggregation queries, cumulative distribution functions, and/or correlation metrics) were applied to each of these datasets. Analysis focused on year-end 2013, but historical data permitted some longitudinal analysis. ResultsWikipedia’s medical content (at the end of 2013) was made up of more than 155,000 articles and 1 billion bytes of text across more than 255 languages. This content was supported by more than 950,000 references. Content was viewed more than 4.88 billion times in 2013. This makes it one of if not the most viewed medical resource(s) globally. The core editor community numbered less than 300 and declined over the past 5 years. The members of this community were half health care providers and 85.5% (100/117) had a university education. ConclusionsAlthough Wikipedia has a considerable volume of multilingual medical content that is extensively read and well-referenced, the core group of editors that contribute and maintain that content is small and shrinking in size.

Background: Patients with chronic conditions require ongoing care which not only necessitates support from health care providers outside appointments but also self-management. Web-based tools for text-based patient-provider communication, such as secure messaging, allow for sharing of contextual information and personal narrative in a simple accessible medium, empowering patients and enabling their providers to address emerging care needs. Objective: The objectives of this study were to (1) conduct a systematic search of the published literature and the Internet for Web-based tools for text-based communication between patients and providers; (2) map tool characteristics, their intended use, contexts in which they were used, and by whom; (3) describe the nature of their evaluation; and (4) understand the terminology used to describe the tools. Methods: We conducted a scoping review using the MEDLINE (Medical Literature Analysis and Retrieval System Online) and EMBASE (Excerpta Medica Database) databases. We summarized information on the characteristics of the tools (structure, functions, and communication paradigm), intended use, context and users, evaluation (study design and outcomes), and terminology. We performed a parallel search of the Internet to compare with tools identified in the published literature. Results: We identified 54 papers describing 47 unique tools from 13 countries studied in the context of 68 chronic health conditions. The majority of tools (77%, 36/47) had functions in addition to communication (eg, viewable care plan, symptom diary, or tracker). Eight tools (17%, 8/47) were described as allowing patients to communicate with the team or multiple health care providers. Most of the tools were intended to support communication regarding symptom reporting (49%, 23/47), and lifestyle or behavior modification (36%, 17/47). The type of health care providers who used tools to communicate with patients were predominantly allied health professionals of various disciplines (30%, 14/47), nurses (23%, 11/47), and physicians (19%, 9/47), among others. Over half (52%, 25/48) of the tools were evaluated in randomized controlled trials, and 23 tools (48%, 23/48) were evaluated in nonrandomized studies. Terminology of tools varied by intervention type and functionality and did not consistently reflect a theme of communication. The majority of tools found in the Internet search were patient portals from 6 developers; none were found among published articles. Conclusions: Web-based tools for text-based patient-provider communication were identified from a wide variety of clinical contexts and with varied functionality. Tools were most prevalent in contexts where intended use was self-management. Few tools for team-based communication were found, but this may become increasingly important as chronic disease care becomes more interdisciplinary. [J Med Internet Res 2017;19(10):e366]

Background:The initial introduction of the World Wide Web in 1990 brought around the biggest change in information acquisition. Due to the abundance of devices and ease of access they subsequently allow, the utility of mobile health (mHealth) has never been more endemic. A substantial amount of interactive and psychoeducational apps are readily available to download concerning a wide range of health issues. mHealth has the potential to reduce waiting times for appointments; eradicate the need to meet in person with a clinician, successively diminishing the workload of mental health professionals; be more cost effective to practices; and encourage self-care tactics. Previous research has given valid evidence with empirical studies proving the effectiveness of physical and mental health interventions using mobile apps. Alongside apps, there is evidence to show that receiving short message service (SMS) messages, which entail psychoeducation, medication reminders, and links to useful informative Web pages can also be advantageous to a patient’s mental and physical well-being. Available mHealth apps and SMS services and their ever improving quality necessitates a systematic review in the area in reference to reduction of symptomology, adherence to intervention, and usability.Objective:The aim of this review was to study the efficacy, usability, and feasibility of mobile apps and SMS messages as mHealth interventions for self-guided care.Methods:A systematic literature search was carried out in JMIR, PubMed, PsychINFO, PsychARTICLES, Google Scholar, MEDLINE, and SAGE. The search spanned from January 2008 to January 2017. The primary outcome measures consisted of weight management, (pregnancy) smoking cessation, medication adherence, depression, anxiety and stress. Where possible, adherence, feasibility, and usability outcomes of the apps or SMS services were evaluated. Between-group and within-group effect sizes (Cohen d) for the mHealth intervention method group were determined.Results:A total of 27 studies, inclusive of 4658 participants were reviewed. The papers included randomized controlled trials (RCTs) (n=19), within-group studies (n=7), and 1 within-group study with qualitative aspect. Studies show improvement in physical health and significant reductions of anxiety, stress, and depression. Within-group and between-group effect sizes ranged from 0.05-3.37 (immediately posttest), 0.05-3.25 (1-month follow-up), 0.08-3.08 (2-month follow-up), 0.00-3.10 (3-month follow-up), and 0.02-0.27 (6-month follow-up). Usability and feasibility of mHealth interventions, where reported, also gave promising, significant results.Conclusions:The review shows the promising and emerging efficacy of using mobile apps and SMS text messaging as mHealth interventions.